From 0d6be0ddd85d1d5452f9115f596d1e19c75d2107 Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Thu, 1 Dec 2022 12:22:51 -0500 Subject: [PATCH 01/20] Create 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 1 + 1 file changed, 1 insertion(+) create mode 100644 content/blog/2022-12-01-epidata-v4.Rmd diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -0,0 +1 @@ + From 898e4eaf8fa31b70159052fb65c121b20641a7a3 Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Thu, 1 Dec 2022 12:23:45 -0500 Subject: [PATCH 02/20] Update 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 160 +++++++++++++++++++++++++ 1 file changed, 160 insertions(+) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 8b1378917..8cc2bbbfb 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -1 +1,161 @@ +--- +title: Hello World! +author: Roni Rosenfeld and Ryan Tibshirani +date: 2020-08-10 +tags: + - COVIDcast +authors: + - roni + - ryan +heroImage: blog-lg-img_hello-world.jpg +heroImageThumb: blog-thumb-img_hello-world.jpg +summary: | + Hello from the Delphi research group at Carnegie Mellon University! + We're a group of faculty, students, and staff, based primarily out of CMU + together with strong collaborators from other universities and industry. + Our group was founded in 2012 to advance the theory and practice of epidemic + forecasting. Since March 2020, we have refocused efforts towards helping combat + the COVID-19 pandemic, by supporting informed decision-making at federal, state, + and local levels of government and in the healthcare sector. Until now, we've + been pretty “heads down” with our work, and slow to communicate what we've been + up to. But at last ... Delphi finally has a blog! This first post serves as an + introduction of sorts. Future posts will dive deeper into our various projects. +output: + blogdown::html_page: + toc: true +--- +Hello from the Delphi research group at Carnegie Mellon University! +We're a group of faculty, students, and staff, based primarily out of CMU +together with strong collaborators from other universities and industry. +Our group was founded in 2012 to advance the theory and practice of epidemic +forecasting. Since March 2020, we have refocused efforts towards helping combat +the COVID-19 pandemic, by supporting informed decision-making at federal, state, +and local levels of government and in the healthcare sector. Until now, we've +been pretty "heads down" with our work, and slow to communicate what we've been +up to. But at last ... Delphi finally has a blog! This first post serves as an +introduction of sorts. Future posts will dive deeper into our various projects. + +## A Little Bit About Us + +We (Roni and Ryan) are co-leads and co-founders of the Delphi group. +When we started the group back in 2012, +our mission was to develop the theory +and practice of epidemiological forecasting, with a long-term +vision of seeing this technology become as universally accepted +and useful as weather forecasting is today. +In the following years, we developed both nowcasting and forecasting models, +focusing primarily on seasonal influenza in the US. +Our forecasting system is a perennial top finisher +in the Centers for Disease Control's annual forecasting challenges. +In 2019, we were named one of the CDC's two +Centers of Excellence for Influenza Forecasting. + +Back when we formed the group in 2012, +the "original four" members were David Farrow, Logan Brooks, Roni, and Ryan. +By early 2020, pre-pandemic, we'd grown to about 7-8 members in size, +and were happily and steadily making progress on flu forecasting and nowcasting, +and making headway into dengue and norovirus. +To learn about the work our group has done in the past, +including some papers we've written, software tools we've built, +and the real-time epidemiological data server we've been deploying since 2016, +check out `r blogdown::shortcode_html("reflink", "/", "our website")`. + +When the COVID-19 pandemic arrived, we focused all our attention on it. +Our Delphi team +quickly grew to 30+ members, and is still growing. +Most of our members are volunteers, +drawing talent from within CMU and several other universities, +including Stanford, UC Davis, and USC, and also from industry. +The pace has been intense and dizzying at times, +and we're infinitely grateful for the contributions +and commitment of all our new members---we'd be nowhere without them. +See `r blogdown::shortcode_html("reflink", "team", "here")` +for a list of the Delphi team members. + +With new members comes a new breadth of expertise: +our expertise now covers statistical modeling, computation, +systems engineering, user interfaces, visualization, +digital epidemiology, and epidemic forecasting. +We've been extremely fortunate to have developed +several new relationships with stakeholders and supporters in government, +the healthcare sector, and the tech industry; and of course, +we're proud and grateful for our existing relationships, +especially our longstanding relationship with the CDC. + +## Our Mission and Strategy + +While Delphi's long-term mission remains to advance the theory and practice of +epidemic forecasting, since March 2020 our goals are to help combat the COVID-19 +pandemic and save lives and livelihoods. +We aim to support informed decision-making at federal, +state, and local levels of government and in the healthcare sector. +Whenever possible, we strive to make our work useful +to the private and public sectors, other researchers, +the press, and the general public. + +Our strategy: + +1. Improve pandemic situational awareness and understanding + by providing comprehensive, geographically-detailed, + and continuously-updated indicators of pandemic activity and its impact, + helping to make meaning out of the pandemic information deluge. + +2. Support local, state, and federal governments' + ongoing decision-making in their attempts to balance + public health concerns with economic preservation, + by providing validated, verifiable, localized, + short-term forecasts of epidemic spread and healthcare demand, + under any assumed level of the local population's mobility and distancing + behavior. + +3. Analyze and demonstrate the impacts of governments' + past tightening or loosening of mitigation measures + (e.g., opening or closing schools or businesses, + imposing or lifting bans on gatherings, shelter in place orders, etc.) + on a population's mobility and distancing behavior. + +4. Engage continuously with our target users to communicate + our findings, and inform our directions and priorities. + +5. Make our products useful and accessible to other + researchers and tool developers, to amplify their impact. + To that end, we'll continue to make everything we invent or produce + publicly and freely available as soon as possible + and to the greatest degree allowed, including models, + algorithms, software, tools, estimates, and forecasts. + +## What We've Been Up To + +So what have we been doing since March? +We've been working towards our strategic goals above, +and have made good progress on some of them. + +Here's a quick summary: + +- We've built a number of new indicators of COVID-19 activity. + These are fine-grained geographically + (most of them are available at the US county level) + and temporally (all of them updated daily). + They are designed to shed light on the current picture of COVID in the US, + beyond the typical publicly-available metrics like confirmed cases and deaths. + +- Some of our indicators are based on massive-scale surveys that we’re running + through partnerships with Facebook and Google, and others are based on + aggregated counts from massive medical claims data sets through partners like + Change Healthcare. + +- We've built a `r blogdown::shortcode_html("apireflink", "api/covidcast.html", "public API")`, + and `r blogdown::shortcode_html("apireflink", "api/covidcast_clients.html", "R and Python packages")`, + to serve our indicators to researchers and the public. This API provides new + data daily. + +- We've built `r blogdown::shortcode_html("reflink", "covidcast", "interactive maps and graphics")` to + display our indicators, and better inform the public and decision-makers. + +- We've developed forecasts of the future spread of the pandemic, + validated them prospectively, and started submitting them to CDC. + +In subsequent posts, we'll dive deeper into many of these projects, and touch on +what's next for us (a lot... in many ways, we feel that our most important work +is still ahead of us). Until next time! From 42ac50da4e98a7fff658c75e6f96a0e794a23c44 Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Sat, 10 Dec 2022 11:17:47 -0500 Subject: [PATCH 03/20] Update 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 244 ++++++++++--------------- 1 file changed, 94 insertions(+), 150 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 8cc2bbbfb..c1c03e5f2 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -1,161 +1,105 @@ --- -title: Hello World! -author: Roni Rosenfeld and Ryan Tibshirani -date: 2020-08-10 +title: Introducing Epidata v4 +author: Kathryn Mazaitis, Brian Clark, George Haff +date: 2022-12-10 tags: - - COVIDcast + - epidata authors: - - roni - - ryan + - kathryn + - brian + - george heroImage: blog-lg-img_hello-world.jpg heroImageThumb: blog-thumb-img_hello-world.jpg summary: | - Hello from the Delphi research group at Carnegie Mellon University! - We're a group of faculty, students, and staff, based primarily out of CMU - together with strong collaborators from other universities and industry. - Our group was founded in 2012 to advance the theory and practice of epidemic - forecasting. Since March 2020, we have refocused efforts towards helping combat - the COVID-19 pandemic, by supporting informed decision-making at federal, state, - and local levels of government and in the healthcare sector. Until now, we've - been pretty “heads down” with our work, and slow to communicate what we've been - up to. But at last ... Delphi finally has a blog! This first post serves as an - introduction of sorts. Future posts will dive deeper into our various projects. + Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the Epidata API. The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. output: blogdown::html_page: toc: true --- -Hello from the Delphi research group at Carnegie Mellon University! -We're a group of faculty, students, and staff, based primarily out of CMU -together with strong collaborators from other universities and industry. -Our group was founded in 2012 to advance the theory and practice of epidemic -forecasting. Since March 2020, we have refocused efforts towards helping combat -the COVID-19 pandemic, by supporting informed decision-making at federal, state, -and local levels of government and in the healthcare sector. Until now, we've -been pretty "heads down" with our work, and slow to communicate what we've been -up to. But at last ... Delphi finally has a blog! This first post serves as an -introduction of sorts. Future posts will dive deeper into our various projects. - -## A Little Bit About Us - -We (Roni and Ryan) are co-leads and co-founders of the Delphi group. -When we started the group back in 2012, -our mission was to develop the theory -and practice of epidemiological forecasting, with a long-term -vision of seeing this technology become as universally accepted -and useful as weather forecasting is today. -In the following years, we developed both nowcasting and forecasting models, -focusing primarily on seasonal influenza in the US. -Our forecasting system is a perennial top finisher -in the Centers for Disease Control's annual forecasting challenges. -In 2019, we were named one of the CDC's two -Centers of Excellence for Influenza Forecasting. - -Back when we formed the group in 2012, -the "original four" members were David Farrow, Logan Brooks, Roni, and Ryan. -By early 2020, pre-pandemic, we'd grown to about 7-8 members in size, -and were happily and steadily making progress on flu forecasting and nowcasting, -and making headway into dengue and norovirus. -To learn about the work our group has done in the past, -including some papers we've written, software tools we've built, -and the real-time epidemiological data server we've been deploying since 2016, -check out `r blogdown::shortcode_html("reflink", "/", "our website")`. - -When the COVID-19 pandemic arrived, we focused all our attention on it. -Our Delphi team -quickly grew to 30+ members, and is still growing. -Most of our members are volunteers, -drawing talent from within CMU and several other universities, -including Stanford, UC Davis, and USC, and also from industry. -The pace has been intense and dizzying at times, -and we're infinitely grateful for the contributions -and commitment of all our new members---we'd be nowhere without them. -See `r blogdown::shortcode_html("reflink", "team", "here")` -for a list of the Delphi team members. - -With new members comes a new breadth of expertise: -our expertise now covers statistical modeling, computation, -systems engineering, user interfaces, visualization, -digital epidemiology, and epidemic forecasting. -We've been extremely fortunate to have developed -several new relationships with stakeholders and supporters in government, -the healthcare sector, and the tech industry; and of course, -we're proud and grateful for our existing relationships, -especially our longstanding relationship with the CDC. - -## Our Mission and Strategy - -While Delphi's long-term mission remains to advance the theory and practice of -epidemic forecasting, since March 2020 our goals are to help combat the COVID-19 -pandemic and save lives and livelihoods. -We aim to support informed decision-making at federal, -state, and local levels of government and in the healthcare sector. -Whenever possible, we strive to make our work useful -to the private and public sectors, other researchers, -the press, and the general public. - -Our strategy: - -1. Improve pandemic situational awareness and understanding - by providing comprehensive, geographically-detailed, - and continuously-updated indicators of pandemic activity and its impact, - helping to make meaning out of the pandemic information deluge. - -2. Support local, state, and federal governments' - ongoing decision-making in their attempts to balance - public health concerns with economic preservation, - by providing validated, verifiable, localized, - short-term forecasts of epidemic spread and healthcare demand, - under any assumed level of the local population's mobility and distancing - behavior. - -3. Analyze and demonstrate the impacts of governments' - past tightening or loosening of mitigation measures - (e.g., opening or closing schools or businesses, - imposing or lifting bans on gatherings, shelter in place orders, etc.) - on a population's mobility and distancing behavior. - -4. Engage continuously with our target users to communicate - our findings, and inform our directions and priorities. - -5. Make our products useful and accessible to other - researchers and tool developers, to amplify their impact. - To that end, we'll continue to make everything we invent or produce - publicly and freely available as soon as possible - and to the greatest degree allowed, including models, - algorithms, software, tools, estimates, and forecasts. - -## What We've Been Up To - -So what have we been doing since March? -We've been working towards our strategic goals above, -and have made good progress on some of them. - -Here's a quick summary: - -- We've built a number of new indicators of COVID-19 activity. - These are fine-grained geographically - (most of them are available at the US county level) - and temporally (all of them updated daily). - They are designed to shed light on the current picture of COVID in the US, - beyond the typical publicly-available metrics like confirmed cases and deaths. - -- Some of our indicators are based on massive-scale surveys that we’re running - through partnerships with Facebook and Google, and others are based on - aggregated counts from massive medical claims data sets through partners like - Change Healthcare. - -- We've built a `r blogdown::shortcode_html("apireflink", "api/covidcast.html", "public API")`, - and `r blogdown::shortcode_html("apireflink", "api/covidcast_clients.html", "R and Python packages")`, - to serve our indicators to researchers and the public. This API provides new - data daily. - -- We've built `r blogdown::shortcode_html("reflink", "covidcast", "interactive maps and graphics")` to - display our indicators, and better inform the public and decision-makers. - -- We've developed forecasts of the future spread of the pandemic, - validated them prospectively, and started submitting them to CDC. - -In subsequent posts, we'll dive deeper into many of these projects, and touch on -what's next for us (a lot... in many ways, we feel that our most important work -is still ahead of us). Until next time! +Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the Epidata API. The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. + +In this post, we will discuss why these changes were needed, describe the new data architecture, and give a brief summary of how v4 performance compares to the previous version. + +## Motivation + +This work was driven by the necessity to fix several specific deficiencies in the initial design of the covidcast table. As is often the case, these were somewhat easy to ignore at first, but soon became more pronounced over time. They can be grouped into three categories: 1) deficiencies affecting consumers of our API, 2) those affecting our ability to perform data ingestion/repairs and develop against the system, and 3) those affecting our ability to perform systems maintenance tasks efficiently. + +### 1. Customer Affecting + +* Slow analytics queries: + + Computing any kind of aggregate summaries in v3 was excessively expensive, with queries frequently requiring hours or even days to complete. Aggregate statistics and other analytics queries are needed in order to help determine the scope of outages (How many rows were added during the period when the bad code was live, and what range of time_values was affected?) as well as quantify the impact of our dataset for project reports and proposals (How much data do we have from each of our different data sources? What is typical latency? How does latency behavior differ for different types of data sources?). + +* Slow latest queries: + + Some data sources experience heavy revisions. The design of the v3 system required the database to scan all available issues of each matching reference date before it could identify the latest issue. The more revisions it needed to discard, the more time needed for the scan. + +* COVIDcast Metadata: + + We compile a list of all COVIDcast-related sources and signals available in the API, along with basic summary statistics such as the dates they are available, their minimum and maximum values, and the geographic levels at which they are reported. This is made available here. + + The query to produce these aggregations was expensive in v3, requiring nearly five hours to run. Ideally we would like to update the metadata each hour when new data is added to the system. + +### 2. Data Ingestion/Repair and Development + +* Is_latest column: + + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. + +* Reduce repetition/normalize + + The v3 table included the full text of the source, signal, geo type, and geo value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo type, geo value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. + +### 3. Systems Maintenance + +* Decrease backup/restore time: + + As our dataset grows, we incur ever increasing penalties to backing up and restoring our database. Frequent backups are key to maintaining reasonable disaster recovery goals. Fast restoration times are desirable so that we may both meet disaster recovery goals, as well as bring up development systems with full datasets for running experiments at scale. + +## System design +Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: +The data source and signal, specifying the providing authority ("JHU" for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). +The geographic type and a specific location (such as "state" & "Pennsylvania" or "county" & "Allegheny"). +The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. +An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). +The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error). + +A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent "issue" for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the "latest issue" of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming. + +![Fig 1: simplified diagram of v3 single-table design, with color coding marking areas improved in v4. [Full v3 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.3.21/src/ddl/covidcast.sql)](TODO) + +Eventually we undertook the challenge of trying to improve our performance and reduce our resource usage – to come up with v4! + +We chose to switch from [MariaDB](TODO) to [Percona Server for MySQL](TODO) for our database engine and management system. Both are variants of the free open source [MySQL](TODO) database, and both provide additional support and features on top of the base product. Percona offered us access to a more consistently developed drop-in replacement for MySQL, along with expertise and knowledge concentrated in both the open source community and Percona the company. Additionally, Percona Server for MySQL is a reliable target for monitoring with Percona’s [PMM](TODO). + +To reduce our data footprint and speed lookups, we took steps to "[normalize](TODO)" our database. This involved redefining our schema to move some columns out of the main table and into tables of their own, which we reference with "[foreign key](TODO)" columns instead. By arranging our data this way, the long and often-repeated text strings that name our sources and signals as well as the geographic locations we cover are only stored once each, and referred to by a number instead. The relatively short numbers take up much less space on disk compared to the text that they replace, and the database can optimize locating records by these numbers more easily than it can for text. We employ two of these dimension tables, one for source and signal, and one for geographic type and value. Using dimension tables efficiently requires [JOIN](TODO) operations when retrieving from the database. We avoided adding these slightly-more-complicated JOIN statements to many places in our code base by hiding them behind [VIEWs](TODO) -- our VIEWs are a shortcut to the JOIN logic that make our new normalized tables look virtually indistinguishable from old monolithic table to our web server code. + +We also added a "latest" table that includes a copy of just the issues that are most recent for all of our data points and removed the "latest issue" flag. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main [website](TODO), so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The "latest table" simplifies our data management responsibilities compared to the "latest flag", in that we don't have to clear a flag from an old row (a previously "latest" issue of data) when a new "latest" row is added -- instead we simply overwrite the old row in our latest table, and the database does this with great ease. + +Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more [indexes](TODO) in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast. + +![Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. [Full v4 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.4.0/src/ddl/v4_schema.sql)](TODO) + +## Performance results +All performance figures were collected in an isolated QA environment. The core dataset was taken from a September, 2021 snapshot of the production Epidata database and used as-is for v3 experiments and converted to v4 format for v4 experiments. Performance experiments for v3 and v4 were both run on the same machine during dedicated time when nothing else was running. Input data for the "Adding new data" experiment was collected from data actually added to the production system in late May 2022. Input data for the query time experiments was sampled from a segment of actual query logs from July and August 2021, stratified by a generalized parameterized query template. Stratification ensured the samples were reasonably representative of actual traffic in terms of how many reference dates, regions, and signals are requested in a single query, as well as whether queries require the full data revision history or only the most recent issue of each reference date. Additional details about query log sampling will be included in a future post. + +Overall, the v4 system improves Epidata performance for adding new data and substantially improves performance for querying the most up-to-date figures. Storage performance is the same, although we suspect we could improve this by removing an index or two without much impact on query performance. Query performance for queries that must touch the data revision history is worse, but not by much; performance should still be adequate for most research use cases (*please do get in touch if this is not the case for you*). + +TODO METRIC TABLE + +TODO QUERY TIME TABLE + +The largest factor in the query time improvements for latest data is a reduction in the frequency and severity of long-running queries. In v3, over seven hundred queries required over ten seconds to compute, and the longest-running query took twelve minutes; in v4 only three queries needed more than ten seconds and no query took more than twenty-one seconds. + +For historical data, long-running queries were more frequent in v4 than in v3, but not more severe. In v3, only 8 queries required more than one second to compute, and the longest-running query took seventeen seconds; in v4 over two hundred queries needed more than one second, but the max query time was sixteen seconds. + +The empirical cumulative distribution functions for query times in the latest and historical datasets shows both of these effects in greater detail. + +TODO LONG-RUNNING QUERIES TABLE + +TODO LATEST DATA IMAGE + +TODO HISTORICAL DATA IMAGE + +## Conclusion + +Epidata v4 represents an enormous and necessary effort in developing scalable access to versioned time series data. We're excited to continue improving in this space! We have plans for: + +* Realizing the timestamp stubs - taking advantage of database optimizations for datetime data, making it possible to track multiple updates each day or reference period, and accurately representing unusual reference periods (such as HHS Friday-Thursday weeks) +* Realizing the stratification stubs - permitting support for age, demographic, serotype, and other data splits +* Bringing up an influenza schema - transferring what we've learned from covid about geotemporally-detailed epidemic surveillance tracking, and proving the modular capabilities of the new system + From 68330603b87218927d8b3bfa6fadef96188233cf Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Mon, 12 Dec 2022 13:38:18 -0500 Subject: [PATCH 04/20] Add files via upload --- .../blog/images/pairwise-historical-data.png | Bin 0 -> 168344 bytes content/blog/images/pairwise-latest-data.png | Bin 0 -> 230614 bytes 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z3ntgPwDeb5Socyv(8Z@q|FJ?D=ecCC9422{={7nq>;N7Uej{>nzR?oHUNT?b+G;-F z0<&7X^PviXZZ1gagL{xCa+6f-c4%g3_pZMJSQa5~*M@*n{C8f09?yg~5=mpYa*i+W zg9Si-+jgulqTCUY?hF`x__WCD0Xd{?578rBL9=`I(q5{H5L6R>F?Q`DU13C?3PU Date: Mon, 12 Dec 2022 14:03:01 -0500 Subject: [PATCH 05/20] Update 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index c1c03e5f2..159780485 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -60,7 +60,7 @@ The value of the data itself (typically a number representing a count or percent A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent "issue" for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the "latest issue" of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming. -![Fig 1: simplified diagram of v3 single-table design, with color coding marking areas improved in v4. [Full v3 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.3.21/src/ddl/covidcast.sql)](TODO) +![Fig 1: simplified diagram of v3 single-table design, with color coding marking areas improved in v4. [Full v3 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.3.21/src/ddl/covidcast.sql)](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/v3-table-design.png) Eventually we undertook the challenge of trying to improve our performance and reduce our resource usage – to come up with v4! @@ -72,7 +72,7 @@ We also added a "latest" table that includes a copy of just the issues that are Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more [indexes](TODO) in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast. -![Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. [Full v4 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.4.0/src/ddl/v4_schema.sql)](TODO) +![Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. [Full v4 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.4.0/src/ddl/v4_schema.sql)](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/v4-table-design.png) ## Performance results All performance figures were collected in an isolated QA environment. The core dataset was taken from a September, 2021 snapshot of the production Epidata database and used as-is for v3 experiments and converted to v4 format for v4 experiments. Performance experiments for v3 and v4 were both run on the same machine during dedicated time when nothing else was running. Input data for the "Adding new data" experiment was collected from data actually added to the production system in late May 2022. Input data for the query time experiments was sampled from a segment of actual query logs from July and August 2021, stratified by a generalized parameterized query template. Stratification ensured the samples were reasonably representative of actual traffic in terms of how many reference dates, regions, and signals are requested in a single query, as well as whether queries require the full data revision history or only the most recent issue of each reference date. Additional details about query log sampling will be included in a future post. @@ -91,9 +91,15 @@ The empirical cumulative distribution functions for query times in the latest an TODO LONG-RUNNING QUERIES TABLE -TODO LATEST DATA IMAGE +![Fig 3: Empirical cumulative distribution functions for query times, latest data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/query-time-latest-data.png) -TODO HISTORICAL DATA IMAGE +![Fig 4: Empirical cumulative distribution functions for query times, historical data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/query-time-historical-data.png) + +If we compare the running time in each system for each query, we can get a more precise picture of whether and how times improve or worsen between the two systems. For queries of latest data, the vast majority of queries are faster in v4, but a few are slower, sometimes by more than 10x. For queries of historical data, v4 and v3 query time are typically similar, with a couple clouds of slower query times in v4. + +![Fig 5: Pairwise query time, latest data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/pairwise-latest-data.png) + +![Fig 6: Pairwise query time, historical data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/pairwise-historical-data.png) ## Conclusion From 97d036113a693130c78a0c4d657ac756750b39f2 Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Mon, 12 Dec 2022 14:57:07 -0500 Subject: [PATCH 06/20] Update 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 159780485..d59223789 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -8,8 +8,7 @@ authors: - kathryn - brian - george -heroImage: blog-lg-img_hello-world.jpg -heroImageThumb: blog-thumb-img_hello-world.jpg +heroImageThumb: blog-thumb.jpg summary: | Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the Epidata API. The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. output: From 93afcf5b67323346b267ecec5fd280ed8ab519dc Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Wed, 14 Dec 2022 14:14:39 -0500 Subject: [PATCH 07/20] Update 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 37 +++++++++++++++++++------- 1 file changed, 27 insertions(+), 10 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index d59223789..a044b8311 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -10,13 +10,13 @@ authors: - george heroImageThumb: blog-thumb.jpg summary: | - Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the Epidata API. The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. + Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the [Epidata API](https://cmu-delphi.github.io/delphi-epidata/). The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. output: blogdown::html_page: toc: true --- -Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the Epidata API. The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. +Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the [Epidata API](https://cmu-delphi.github.io/delphi-epidata/). The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. In this post, we will discuss why these changes were needed, describe the new data architecture, and give a brief summary of how v4 performance compares to the previous version. @@ -63,13 +63,13 @@ A small single-table design can be manageable, but our data set grew very quickl Eventually we undertook the challenge of trying to improve our performance and reduce our resource usage – to come up with v4! -We chose to switch from [MariaDB](TODO) to [Percona Server for MySQL](TODO) for our database engine and management system. Both are variants of the free open source [MySQL](TODO) database, and both provide additional support and features on top of the base product. Percona offered us access to a more consistently developed drop-in replacement for MySQL, along with expertise and knowledge concentrated in both the open source community and Percona the company. Additionally, Percona Server for MySQL is a reliable target for monitoring with Percona’s [PMM](TODO). +We chose to switch from [MariaDB](https://mariadb.org) to [Percona Server for MySQL](https://www.percona.com/software/mysql-database/percona-server) for our database engine and management system. Both are variants of the free open source [MySQL](https://www.mysql.com) database, and both provide additional support and features on top of the base product. Percona offered us access to a more consistently developed drop-in replacement for MySQL, along with expertise and knowledge concentrated in both the open source community and Percona the company. Additionally, Percona Server for MySQL is a reliable target for monitoring with Percona’s [PMM](https://docs.percona.com/percona-monitoring-and-management/). -To reduce our data footprint and speed lookups, we took steps to "[normalize](TODO)" our database. This involved redefining our schema to move some columns out of the main table and into tables of their own, which we reference with "[foreign key](TODO)" columns instead. By arranging our data this way, the long and often-repeated text strings that name our sources and signals as well as the geographic locations we cover are only stored once each, and referred to by a number instead. The relatively short numbers take up much less space on disk compared to the text that they replace, and the database can optimize locating records by these numbers more easily than it can for text. We employ two of these dimension tables, one for source and signal, and one for geographic type and value. Using dimension tables efficiently requires [JOIN](TODO) operations when retrieving from the database. We avoided adding these slightly-more-complicated JOIN statements to many places in our code base by hiding them behind [VIEWs](TODO) -- our VIEWs are a shortcut to the JOIN logic that make our new normalized tables look virtually indistinguishable from old monolithic table to our web server code. +To reduce our data footprint and speed lookups, we took steps to "[normalize](https://en.wikipedia.org/wiki/Database_normalization)" our database. This involved redefining our schema to move some columns out of the main table and into tables of their own, which we reference with "[foreign key](https://en.wikipedia.org/wiki/Foreign_key)" columns instead. By arranging our data this way, the long and often-repeated text strings that name our sources and signals as well as the geographic locations we cover are only stored once each, and referred to by a number instead. The relatively short numbers take up much less space on disk compared to the text that they replace, and the database can optimize locating records by these numbers more easily than it can for text. We employ two of these dimension tables, one for source and signal, and one for geographic type and value. Using dimension tables efficiently requires [JOIN](https://en.wikipedia.org/wiki/Join_(SQL)) operations when retrieving from the database. We avoided adding these slightly-more-complicated JOIN statements to many places in our code base by hiding them behind [VIEWs](https://en.wikipedia.org/wiki/View_(SQL)) -- our VIEWs are a shortcut to the JOIN logic that make our new normalized tables look virtually indistinguishable from old monolithic table to our web server code. -We also added a "latest" table that includes a copy of just the issues that are most recent for all of our data points and removed the "latest issue" flag. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main [website](TODO), so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The "latest table" simplifies our data management responsibilities compared to the "latest flag", in that we don't have to clear a flag from an old row (a previously "latest" issue of data) when a new "latest" row is added -- instead we simply overwrite the old row in our latest table, and the database does this with great ease. +We also added a "latest" table that includes a copy of just the issues that are most recent for all of our data points and removed the "latest issue" flag. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main [website](https://delphi.cmu.edu/covidcast/), so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The "latest table" simplifies our data management responsibilities compared to the "latest flag", in that we don't have to clear a flag from an old row (a previously "latest" issue of data) when a new "latest" row is added -- instead we simply overwrite the old row in our latest table, and the database does this with great ease. -Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more [indexes](TODO) in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast. +Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more [indexes](https://en.wikipedia.org/wiki/Database_index) in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast. ![Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. [Full v4 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.4.0/src/ddl/v4_schema.sql)](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/v4-table-design.png) @@ -78,9 +78,21 @@ All performance figures were collected in an isolated QA environment. The core d Overall, the v4 system improves Epidata performance for adding new data and substantially improves performance for querying the most up-to-date figures. Storage performance is the same, although we suspect we could improve this by removing an index or two without much impact on query performance. Query performance for queries that must touch the data revision history is worse, but not by much; performance should still be adequate for most research use cases (*please do get in touch if this is not the case for you*). -TODO METRIC TABLE - -TODO QUERY TIME TABLE +Metric | v4 performance relative to v3 | +--- | --- | --- | +Disk footprint | same | +Adding new data | 30% faster | +Querying latest data, i.e., the most recent issue of any reference date | 96% faster | +Querying historical data, e.g. for as-of, issue, or lag queries | 120% slower | + +Query time (seconds) | Latest | Historical | +--- | --- | --- | +Percentile | v3 | v4 | v3 | v4 | +0th (minimum) | 0.005 | 0.005 | 0.005 | 0 .005 | +25th | 0.007 | 0.006 | 0.006 | 0.007 | +50th (median) | 0.009 | 0.008 | 0.007 | 0.008 | +75th | 0.058 | 0.017 | 0.012 | 0.023 | +100th (maximum) | 739.332 | 20.069 | 17.121 | 15.997 | The largest factor in the query time improvements for latest data is a reduction in the frequency and severity of long-running queries. In v3, over seven hundred queries required over ten seconds to compute, and the longest-running query took twelve minutes; in v4 only three queries needed more than ten seconds and no query took more than twenty-one seconds. @@ -88,7 +100,12 @@ For historical data, long-running queries were more frequent in v4 than in v3, b The empirical cumulative distribution functions for query times in the latest and historical datasets shows both of these effects in greater detail. -TODO LONG-RUNNING QUERIES TABLE +Long-running queries (counts) | Latest (of 37,396) | Historical (of 19,359) | +--- | --- | --- | +Queries slower than | v3 | v4 | v3 | v4 | +1 second | 2734 | 17 | 8 | 228 | +10 seconds | 706 | 3 | 1 | 1 | +100 seconds | 3 | 0 | 0 | 0 | ![Fig 3: Empirical cumulative distribution functions for query times, latest data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/query-time-latest-data.png) From 3c020ad57f69e8c896c84003be815e301986b08c Mon Sep 17 00:00:00 2001 From: Carlyn Van Dyke <62626254+carlynvandyke@users.noreply.github.com> Date: Wed, 14 Dec 2022 14:16:12 -0500 Subject: [PATCH 08/20] Update 2022-12-01-epidata-v4.Rmd --- content/blog/2022-12-01-epidata-v4.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index a044b8311..13511afcc 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -1,7 +1,7 @@ --- title: Introducing Epidata v4 author: Kathryn Mazaitis, Brian Clark, George Haff -date: 2022-12-10 +date: 2022-12-14 tags: - epidata authors: From 9e032e13d490cfc15df04a705245f40673bc7ec3 Mon Sep 17 00:00:00 2001 From: minhkhul Date: Fri, 20 Jan 2023 14:10:24 -0500 Subject: [PATCH 09/20] moved static content under static/blog and generated html for blog post --- content/blog/2022-12-01-epidata-v4.Rmd | 15 +- content/blog/2022-12-01-epidata-v4.html | 250 ++++++++++++++++++ .../pairwise-historical-data.png | Bin .../pairwise-latest-data.png | Bin .../query-time-historical-data.png | Bin .../query-time-latest-data.png | Bin .../v3-table-design.png | Bin .../v4-table-design.png | Bin 8 files changed, 258 insertions(+), 7 deletions(-) create mode 100644 content/blog/2022-12-01-epidata-v4.html rename {content/blog/images => static/blog/2022-12-01-epidata-v4_files}/pairwise-historical-data.png (100%) rename {content/blog/images => static/blog/2022-12-01-epidata-v4_files}/pairwise-latest-data.png (100%) rename {content/blog/images => static/blog/2022-12-01-epidata-v4_files}/query-time-historical-data.png (100%) rename {content/blog/images => static/blog/2022-12-01-epidata-v4_files}/query-time-latest-data.png (100%) rename {content/blog/images => static/blog/2022-12-01-epidata-v4_files}/v3-table-design.png (100%) rename {content/blog/images => static/blog/2022-12-01-epidata-v4_files}/v4-table-design.png (100%) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 13511afcc..43b098766 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -8,7 +8,8 @@ authors: - kathryn - brian - george -heroImageThumb: blog-thumb.jpg +heroImage: blog-lg-img_hello-world.jpg +heroImageThumb: blog-thumb-img_hello-world.jpg summary: | Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the [Epidata API](https://cmu-delphi.github.io/delphi-epidata/). The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. output: @@ -59,7 +60,7 @@ The value of the data itself (typically a number representing a count or percent A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent "issue" for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the "latest issue" of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming. -![Fig 1: simplified diagram of v3 single-table design, with color coding marking areas improved in v4. [Full v3 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.3.21/src/ddl/covidcast.sql)](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/v3-table-design.png) +![Fig 1: simplified diagram of v3 single-table design, with color coding marking areas improved in v4. [Full v3 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.3.21/src/ddl/covidcast.sql)](/blog/2022-12-01-epidata-v4_files/v3-table-design.png) Eventually we undertook the challenge of trying to improve our performance and reduce our resource usage – to come up with v4! @@ -71,7 +72,7 @@ We also added a "latest" table that includes a copy of just the issues that are Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more [indexes](https://en.wikipedia.org/wiki/Database_index) in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast. -![Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. [Full v4 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.4.0/src/ddl/v4_schema.sql)](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/v4-table-design.png) +![Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. [Full v4 schema details available on GitHub.](https://github.com/cmu-delphi/delphi-epidata/blob/v0.4.0/src/ddl/v4_schema.sql)](/blog/2022-12-01-epidata-v4_files/v4-table-design.png) ## Performance results All performance figures were collected in an isolated QA environment. The core dataset was taken from a September, 2021 snapshot of the production Epidata database and used as-is for v3 experiments and converted to v4 format for v4 experiments. Performance experiments for v3 and v4 were both run on the same machine during dedicated time when nothing else was running. Input data for the "Adding new data" experiment was collected from data actually added to the production system in late May 2022. Input data for the query time experiments was sampled from a segment of actual query logs from July and August 2021, stratified by a generalized parameterized query template. Stratification ensured the samples were reasonably representative of actual traffic in terms of how many reference dates, regions, and signals are requested in a single query, as well as whether queries require the full data revision history or only the most recent issue of each reference date. Additional details about query log sampling will be included in a future post. @@ -107,15 +108,15 @@ Queries slower than | v3 | v4 | v3 | v4 | 10 seconds | 706 | 3 | 1 | 1 | 100 seconds | 3 | 0 | 0 | 0 | -![Fig 3: Empirical cumulative distribution functions for query times, latest data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/query-time-latest-data.png) +![Fig 3: Empirical cumulative distribution functions for query times, latest data](/blog/2022-12-01-epidata-v4_files/query-time-latest-data.png) -![Fig 4: Empirical cumulative distribution functions for query times, historical data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/query-time-historical-data.png) +![Fig 4: Empirical cumulative distribution functions for query times, historical data](/blog/2022-12-01-epidata-v4_files/query-time-historical-data.png) If we compare the running time in each system for each query, we can get a more precise picture of whether and how times improve or worsen between the two systems. For queries of latest data, the vast majority of queries are faster in v4, but a few are slower, sometimes by more than 10x. For queries of historical data, v4 and v3 query time are typically similar, with a couple clouds of slower query times in v4. -![Fig 5: Pairwise query time, latest data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/pairwise-latest-data.png) +![Fig 5: Pairwise query time, latest data](/blog/2022-12-01-epidata-v4_files/pairwise-latest-data.png) -![Fig 6: Pairwise query time, historical data](https://github.com/cmu-delphi/www-main/blob/krivard/blog-tests-more/content/blog/images/pairwise-historical-data.png) +![Fig 6: Pairwise query time, historical data](/blog/2022-12-01-epidata-v4_files/pairwise-historical-data.png) ## Conclusion diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html new file mode 100644 index 000000000..33a1974fc --- /dev/null +++ b/content/blog/2022-12-01-epidata-v4.html @@ -0,0 +1,250 @@ +--- +title: Introducing Epidata v4 +author: Kathryn Mazaitis, Brian Clark, George Haff +date: 2022-12-14 +tags: + - epidata +authors: + - kathryn + - brian + - george +heroImage: blog-lg-img_hello-world.jpg +heroImageThumb: blog-thumb-img_hello-world.jpg +summary: | + Epidata v0.4.0 ("v4" for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the [Epidata API](https://cmu-delphi.github.io/delphi-epidata/). The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification. +output: + blogdown::html_page: + toc: true +--- + + + + +

    Epidata v0.4.0 (“v4” for short) launched on September 26, 2022, bringing about a major revision to how we store data served by the Epidata API. The changes prioritize fast access to the most up-to-date data while retaining the deep data revision history needed by researchers. The launch included a prototype of a modular data organization system intended to generalize across multiple pathogens, with stubs for more advanced and efficient timestamping and greater flexibility in data stratification.

    +

    In this post, we will discuss why these changes were needed, describe the new data architecture, and give a brief summary of how v4 performance compares to the previous version.

    +
    +

    Motivation

    +

    This work was driven by the necessity to fix several specific deficiencies in the initial design of the covidcast table. As is often the case, these were somewhat easy to ignore at first, but soon became more pronounced over time. They can be grouped into three categories: 1) deficiencies affecting consumers of our API, 2) those affecting our ability to perform data ingestion/repairs and develop against the system, and 3) those affecting our ability to perform systems maintenance tasks efficiently.

    +
    +

    1. Customer Affecting

    +
      +
    • Slow analytics queries: +
        +
      • Computing any kind of aggregate summaries in v3 was excessively expensive, with queries frequently requiring hours or even days to complete. Aggregate statistics and other analytics queries are needed in order to help determine the scope of outages (How many rows were added during the period when the bad code was live, and what range of time_values was affected?) as well as quantify the impact of our dataset for project reports and proposals (How much data do we have from each of our different data sources? What is typical latency? How does latency behavior differ for different types of data sources?).
        • +
      • +
    • Slow latest queries: +
        +
      • Some data sources experience heavy revisions. The design of the v3 system required the database to scan all available issues of each matching reference date before it could identify the latest issue. The more revisions it needed to discard, the more time needed for the scan.
        • +
      • +
    • COVIDcast Metadata: +
        +
      • We compile a list of all COVIDcast-related sources and signals available in the API, along with basic summary statistics such as the dates they are available, their minimum and maximum values, and the geographic levels at which they are reported. This is made available here.
        • +
      • The query to produce these aggregations was expensive in v3, requiring nearly five hours to run. Ideally we would like to update the metadata each hour when new data is added to the system.
        • +
      • +
    +
    +
    +

    2. Data Ingestion/Repair and Development

    +
      +
    • Is_latest column: +
        +
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
        • +
      • +
    • Reduce repetition/normalize +
        +
      • The v3 table included the full text of the source, signal, geo type, and geo value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo type, geo value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.
        • +
      • +
    +
    +
    +

    3. Systems Maintenance

    +
      +
    • Decrease backup/restore time: +
        +
      • As our dataset grows, we incur ever increasing penalties to backing up and restoring our database. Frequent backups are key to maintaining reasonable disaster recovery goals. Fast restoration times are desirable so that we may both meet disaster recovery goals, as well as bring up development systems with full datasets for running experiments at scale.
        • +
      • +
    +
    +
    +
    +

    System design

    +

    Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: +The data source and signal, specifying the providing authority (“JHU” for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). +The geographic type and a specific location (such as “state” & “Pennsylvania” or “county” & “Allegheny”). +The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. +An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). +The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error).

    +

    A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent “issue” for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the “latest issue” of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming.

    +
    + +

    Fig 1: simplified diagram of v3 single-table design, with color coding marking areas improved in v4. Full v3 schema details available on GitHub.

    +
    +

    Eventually we undertook the challenge of trying to improve our performance and reduce our resource usage – to come up with v4!

    +

    We chose to switch from MariaDB to Percona Server for MySQL for our database engine and management system. Both are variants of the free open source MySQL database, and both provide additional support and features on top of the base product. Percona offered us access to a more consistently developed drop-in replacement for MySQL, along with expertise and knowledge concentrated in both the open source community and Percona the company. Additionally, Percona Server for MySQL is a reliable target for monitoring with Percona’s PMM.

    +

    To reduce our data footprint and speed lookups, we took steps to “normalize” our database. This involved redefining our schema to move some columns out of the main table and into tables of their own, which we reference with “foreign key” columns instead. By arranging our data this way, the long and often-repeated text strings that name our sources and signals as well as the geographic locations we cover are only stored once each, and referred to by a number instead. The relatively short numbers take up much less space on disk compared to the text that they replace, and the database can optimize locating records by these numbers more easily than it can for text. We employ two of these dimension tables, one for source and signal, and one for geographic type and value. Using dimension tables efficiently requires JOIN operations when retrieving from the database. We avoided adding these slightly-more-complicated JOIN statements to many places in our code base by hiding them behind VIEWs – our VIEWs are a shortcut to the JOIN logic that make our new normalized tables look virtually indistinguishable from old monolithic table to our web server code.

    +

    We also added a “latest” table that includes a copy of just the issues that are most recent for all of our data points and removed the “latest issue” flag. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main website, so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The “latest table” simplifies our data management responsibilities compared to the “latest flag”, in that we don’t have to clear a flag from an old row (a previously “latest” issue of data) when a new “latest” row is added – instead we simply overwrite the old row in our latest table, and the database does this with great ease.

    +

    Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more indexes in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast.

    +
    + +

    Fig 2: simplified diagram of v4 design, showing dimension tables with foreign key relationships and latest/full table scheme. Full v4 schema details available on GitHub.

    +
    +
    +
    +

    Performance results

    +

    All performance figures were collected in an isolated QA environment. The core dataset was taken from a September, 2021 snapshot of the production Epidata database and used as-is for v3 experiments and converted to v4 format for v4 experiments. Performance experiments for v3 and v4 were both run on the same machine during dedicated time when nothing else was running. Input data for the “Adding new data” experiment was collected from data actually added to the production system in late May 2022. Input data for the query time experiments was sampled from a segment of actual query logs from July and August 2021, stratified by a generalized parameterized query template. Stratification ensured the samples were reasonably representative of actual traffic in terms of how many reference dates, regions, and signals are requested in a single query, as well as whether queries require the full data revision history or only the most recent issue of each reference date. Additional details about query log sampling will be included in a future post.

    +

    Overall, the v4 system improves Epidata performance for adding new data and substantially improves performance for querying the most up-to-date figures. Storage performance is the same, although we suspect we could improve this by removing an index or two without much impact on query performance. Query performance for queries that must touch the data revision history is worse, but not by much; performance should still be adequate for most research use cases (please do get in touch if this is not the case for you).

    + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Metricv4 performance relative to v3
    Disk footprintsame
    Adding new data30% faster
    Querying latest data, i.e., the most recent issue of any reference date96% faster
    Querying historical data, e.g. for as-of, issue, or lag queries120% slower
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Query time (seconds)LatestHistorical
    Percentilev3v4
    0th (minimum)0.0050.005
    25th0.0070.006
    50th (median)0.0090.008
    75th0.0580.017
    100th (maximum)739.33220.069
    +

    The largest factor in the query time improvements for latest data is a reduction in the frequency and severity of long-running queries. In v3, over seven hundred queries required over ten seconds to compute, and the longest-running query took twelve minutes; in v4 only three queries needed more than ten seconds and no query took more than twenty-one seconds.

    +

    For historical data, long-running queries were more frequent in v4 than in v3, but not more severe. In v3, only 8 queries required more than one second to compute, and the longest-running query took seventeen seconds; in v4 over two hundred queries needed more than one second, but the max query time was sixteen seconds.

    +

    The empirical cumulative distribution functions for query times in the latest and historical datasets shows both of these effects in greater detail.

    + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Long-running queries (counts)Latest (of 37,396)Historical (of 19,359)
    Queries slower thanv3v4
    1 second273417
    10 seconds7063
    100 seconds30
    +
    + +

    Fig 3: Empirical cumulative distribution functions for query times, latest data

    +
    +
    + +

    Fig 4: Empirical cumulative distribution functions for query times, historical data

    +
    +

    If we compare the running time in each system for each query, we can get a more precise picture of whether and how times improve or worsen between the two systems. For queries of latest data, the vast majority of queries are faster in v4, but a few are slower, sometimes by more than 10x. For queries of historical data, v4 and v3 query time are typically similar, with a couple clouds of slower query times in v4.

    +
    + +

    Fig 5: Pairwise query time, latest data

    +
    +
    + +

    Fig 6: Pairwise query time, historical data

    +
    +
    +
    +

    Conclusion

    +

    Epidata v4 represents an enormous and necessary effort in developing scalable access to versioned time series data. We’re excited to continue improving in this space! We have plans for:

    +
      +
    • Realizing the timestamp stubs - taking advantage of database optimizations for datetime data, making it possible to track multiple updates each day or reference period, and accurately representing unusual reference periods (such as HHS Friday-Thursday weeks)
      • +
    • Realizing the stratification stubs - permitting support for age, demographic, serotype, and other data splits
      • +
    • Bringing up an influenza schema - transferring what we’ve learned from covid about geotemporally-detailed epidemic surveillance tracking, and proving the modular capabilities of the new system
      • +
    +
    diff --git a/content/blog/images/pairwise-historical-data.png b/static/blog/2022-12-01-epidata-v4_files/pairwise-historical-data.png similarity index 100% rename from content/blog/images/pairwise-historical-data.png rename to static/blog/2022-12-01-epidata-v4_files/pairwise-historical-data.png diff --git a/content/blog/images/pairwise-latest-data.png b/static/blog/2022-12-01-epidata-v4_files/pairwise-latest-data.png similarity index 100% rename from content/blog/images/pairwise-latest-data.png rename to static/blog/2022-12-01-epidata-v4_files/pairwise-latest-data.png diff --git a/content/blog/images/query-time-historical-data.png b/static/blog/2022-12-01-epidata-v4_files/query-time-historical-data.png similarity index 100% rename from content/blog/images/query-time-historical-data.png rename to static/blog/2022-12-01-epidata-v4_files/query-time-historical-data.png diff --git a/content/blog/images/query-time-latest-data.png b/static/blog/2022-12-01-epidata-v4_files/query-time-latest-data.png similarity index 100% rename from content/blog/images/query-time-latest-data.png rename to static/blog/2022-12-01-epidata-v4_files/query-time-latest-data.png diff --git a/content/blog/images/v3-table-design.png b/static/blog/2022-12-01-epidata-v4_files/v3-table-design.png similarity index 100% rename from content/blog/images/v3-table-design.png rename to static/blog/2022-12-01-epidata-v4_files/v3-table-design.png diff --git a/content/blog/images/v4-table-design.png b/static/blog/2022-12-01-epidata-v4_files/v4-table-design.png similarity index 100% rename from content/blog/images/v4-table-design.png rename to static/blog/2022-12-01-epidata-v4_files/v4-table-design.png From 3573f27372b60f22bb5cc3eb2d013fdb1e378607 Mon Sep 17 00:00:00 2001 From: Kathryn Mazaitis Date: Mon, 23 Jan 2023 10:40:52 -0500 Subject: [PATCH 10/20] Updated descriptions for blog author listings --- content/people/index.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/content/people/index.md b/content/people/index.md index e0e6a93ee..958f4aedc 100644 --- a/content/people/index.md +++ b/content/people/index.md @@ -139,6 +139,8 @@ people: team: - core - highlight + - blog + description: specializes in Delphi's compute infrastructure, and is a DevOps Engineer in the Machine Learning Department at CMU. - firstName: Sarah lastName: Colquhoun image: sarah-colquhoun_head-shot.jpg @@ -262,6 +264,8 @@ people: affiliation: CMU/CSD team: - core + - blog + description: specializes in Delphi's database and API, and is a Systems Software Engineer in the Machine Learning Department at CMU. - firstName: Robin lastName: Han image: robin-han-500x500-min.jpg @@ -497,7 +501,7 @@ people: - highlight - blog link: https://cs.cmu.edu/~krivard - description: manages Delphi's engineering team, and is a Senior Research Programmer in the Machine Learning Department at CMU. + description: leads Delphi's engineering team, and is a Principal Research Programmer in the Machine Learning Department at CMU. - firstName: Daniel lastName: McDonald image: daniel-mcdonald-photo.jpeg From 208ea7eb144fec36786fd5384badc125a5988440 Mon Sep 17 00:00:00 2001 From: minhkhul <118945681+minhkhul@users.noreply.github.com> Date: Mon, 23 Jan 2023 15:25:56 -0500 Subject: [PATCH 11/20] Update content/blog/2022-12-01-epidata-v4.Rmd Co-authored-by: Katie Mazaitis --- content/blog/2022-12-01-epidata-v4.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 43b098766..bfb76d729 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -48,7 +48,7 @@ This work was driven by the necessity to fix several specific deficiencies in th ### 3. Systems Maintenance * Decrease backup/restore time: - + As our dataset grows, we incur ever increasing penalties to backing up and restoring our database. Frequent backups are key to maintaining reasonable disaster recovery goals. Fast restoration times are desirable so that we may both meet disaster recovery goals, as well as bring up development systems with full datasets for running experiments at scale. + + As our dataset grows, we incur ever increasing penalties to backing up and restoring our database. Frequent backups are key to maintaining reasonable disaster recovery goals. Fast restoration times are desirable so that we may both minimize disruptions associated with data or infrastructure disasters, as well as bring up development systems with full datasets for running experiments at scale. ## System design Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: From 82f0ce9f75ec38978c3ca73681b78da776eb55b7 Mon Sep 17 00:00:00 2001 From: minhkhul <118945681+minhkhul@users.noreply.github.com> Date: Mon, 23 Jan 2023 15:26:21 -0500 Subject: [PATCH 12/20] Update content/blog/2022-12-01-epidata-v4.Rmd Co-authored-by: Katie Mazaitis --- content/blog/2022-12-01-epidata-v4.Rmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index bfb76d729..0e460e195 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -52,11 +52,11 @@ This work was driven by the necessity to fix several specific deficiencies in th ## System design Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: -The data source and signal, specifying the providing authority ("JHU" for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). -The geographic type and a specific location (such as "state" & "Pennsylvania" or "county" & "Allegheny"). -The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. -An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). -The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error). +* The data source and signal, specifying the providing authority ("JHU" for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). +* The geographic type and a specific location (such as "state" & "Pennsylvania" or "county" & "Allegheny"). +* The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. +* An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). +* The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error). A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent "issue" for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the "latest issue" of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming. From 518ace025eabd2149daa9dae9cf3f23431bc8e0c Mon Sep 17 00:00:00 2001 From: minhkhul Date: Tue, 24 Jan 2023 11:48:57 -0500 Subject: [PATCH 13/20] clarify tables --- content/blog/2022-12-01-epidata-v4.Rmd | 74 +++++++--- content/blog/2022-12-01-epidata-v4.html | 178 ++++++++++++++---------- 2 files changed, 160 insertions(+), 92 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 0e460e195..be63eb6fe 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -79,34 +79,66 @@ All performance figures were collected in an isolated QA environment. The core d Overall, the v4 system improves Epidata performance for adding new data and substantially improves performance for querying the most up-to-date figures. Storage performance is the same, although we suspect we could improve this by removing an index or two without much impact on query performance. Query performance for queries that must touch the data revision history is worse, but not by much; performance should still be adequate for most research use cases (*please do get in touch if this is not the case for you*). -Metric | v4 performance relative to v3 | ---- | --- | --- | -Disk footprint | same | -Adding new data | 30% faster | -Querying latest data, i.e., the most recent issue of any reference date | 96% faster | -Querying historical data, e.g. for as-of, issue, or lag queries | 120% slower | - -Query time (seconds) | Latest | Historical | ---- | --- | --- | -Percentile | v3 | v4 | v3 | v4 | -0th (minimum) | 0.005 | 0.005 | 0.005 | 0 .005 | -25th | 0.007 | 0.006 | 0.006 | 0.007 | -50th (median) | 0.009 | 0.008 | 0.007 | 0.008 | -75th | 0.058 | 0.017 | 0.012 | 0.023 | -100th (maximum) | 739.332 | 20.069 | 17.121 | 15.997 | + +
    ++---------------------------------+-------------------------------+ +| Metric | v4 performance relative to v3 | ++:================================+:==============================+ +| Disk footprint | same | ++---------------------------------+-------------------------------+ +| Adding new data | 30% faster | ++---------------------------------+-------------------------------+ +| Querying latest data, i.e. | 96% faster | +| the most recent issue | | +| of any reference date | | ++---------------------------------+-------------------------------+ +| Querying historical data, e.g. | 120% slower | +| for as-of, issue, or lag queries| | ++---------------------------------+-------------------------------+ +
    The largest factor in the query time improvements for latest data is a reduction in the frequency and severity of long-running queries. In v3, over seven hundred queries required over ten seconds to compute, and the longest-running query took twelve minutes; in v4 only three queries needed more than ten seconds and no query took more than twenty-one seconds. + +
    ++---------------------+---------------------+---------------------+ +| Query time | Latest | Historical | +| (seconds) | | | ++---------------------+----------+----------+----------+----------+ +| Percentile | v3 | v4 | v3 | v4 | ++:====================+:========:+:========:+:========:+:========:+ +| 0th (minimum) | 0.005 | 0.005 | 0.005 | 0 .005 | ++---------------------+----------+----------+----------+----------+ +| 25th | 0.007 | 0.006 | 0.006 | 0.007 | ++---------------------+----------+----------+----------+----------+ +| 50th (median) | 0.009 | 0.008 | 0.007 | 0.008 | ++---------------------+----------+----------+----------+----------+ +| 75th | 0.058 | 0.017 | 0.012 | 0.023 | ++---------------------+----------+----------+----------+----------+ +| 100th (maximum) | 739.332 | 20.069 | 17.121 | 15.997 | ++---------------------+----------+----------+----------+----------+ +
    + For historical data, long-running queries were more frequent in v4 than in v3, but not more severe. In v3, only 8 queries required more than one second to compute, and the longest-running query took seventeen seconds; in v4 over two hundred queries needed more than one second, but the max query time was sixteen seconds. The empirical cumulative distribution functions for query times in the latest and historical datasets shows both of these effects in greater detail. -Long-running queries (counts) | Latest (of 37,396) | Historical (of 19,359) | ---- | --- | --- | -Queries slower than | v3 | v4 | v3 | v4 | -1 second | 2734 | 17 | 8 | 228 | -10 seconds | 706 | 3 | 1 | 1 | -100 seconds | 3 | 0 | 0 | 0 | + +
    ++---------------------+---------------------+---------------------+ +| Long-running | Latest | Historical | +| queries (counts) | (of 37,396) | (of 19,359) | ++---------------------+----------+----------+----------+----------+ +|Queries slower than | v3 | v4 | v3 | v4 | ++:====================+:========:+:========:+:========:+:========:+ +| 1 second | 2734 | 17 | 8 | 228 | ++---------------------+----------+----------+----------+----------+ +| 10 seconds | 706 | 3 | 1 | 1 | ++---------------------+----------+----------+----------+----------+ +| 100 seconds | 3 | 0 | 0 | 0 | ++---------------------+----------+----------+----------+----------+ +
    + ![Fig 3: Empirical cumulative distribution functions for query times, latest data](/blog/2022-12-01-epidata-v4_files/query-time-latest-data.png) diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html index 33a1974fc..ae6116876 100644 --- a/content/blog/2022-12-01-epidata-v4.html +++ b/content/blog/2022-12-01-epidata-v4.html @@ -73,7 +73,7 @@

    3. Systems Maintenance

    • Decrease backup/restore time:
        -
      • As our dataset grows, we incur ever increasing penalties to backing up and restoring our database. Frequent backups are key to maintaining reasonable disaster recovery goals. Fast restoration times are desirable so that we may both meet disaster recovery goals, as well as bring up development systems with full datasets for running experiments at scale.
        • +
      • As our dataset grows, we incur ever increasing penalties to backing up and restoring our database. Frequent backups are key to maintaining reasonable disaster recovery goals. Fast restoration times are desirable so that we may both minimize disruptions associated with data or infrastructure disasters, as well as bring up development systems with full datasets for running experiments at scale.
    @@ -81,11 +81,11 @@

    3. Systems Maintenance

    System design

    Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: -The data source and signal, specifying the providing authority (“JHU” for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). -The geographic type and a specific location (such as “state” & “Pennsylvania” or “county” & “Allegheny”). -The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. -An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). -The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error).

    +* The data source and signal, specifying the providing authority (“JHU” for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). +* The geographic type and a specific location (such as “state” & “Pennsylvania” or “county” & “Allegheny”). +* The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. +* An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). +* The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error).

    A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent “issue” for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the “latest issue” of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming.

    @@ -105,122 +105,158 @@

    System design

    Performance results

    All performance figures were collected in an isolated QA environment. The core dataset was taken from a September, 2021 snapshot of the production Epidata database and used as-is for v3 experiments and converted to v4 format for v4 experiments. Performance experiments for v3 and v4 were both run on the same machine during dedicated time when nothing else was running. Input data for the “Adding new data” experiment was collected from data actually added to the production system in late May 2022. Input data for the query time experiments was sampled from a segment of actual query logs from July and August 2021, stratified by a generalized parameterized query template. Stratification ensured the samples were reasonably representative of actual traffic in terms of how many reference dates, regions, and signals are requested in a single query, as well as whether queries require the full data revision history or only the most recent issue of each reference date. Additional details about query log sampling will be included in a future post.

    Overall, the v4 system improves Epidata performance for adding new data and substantially improves performance for querying the most up-to-date figures. Storage performance is the same, although we suspect we could improve this by removing an index or two without much impact on query performance. Query performance for queries that must touch the data revision history is worse, but not by much; performance should still be adequate for most research use cases (please do get in touch if this is not the case for you).

    - +
    +
    ---++ - - - + + - - - + + - - - + + - - - + + - - - + +
    Metricv4 performance relative to v3Metricv4 performance relative to v3
    Disk footprintsameDisk footprintsame
    Adding new data30% fasterAdding new data30% faster
    Querying latest data, i.e., the most recent issue of any reference date96% fasterQuerying latest data, i.e. +the most recent issue +of any reference date96% faster
    Querying historical data, e.g. for as-of, issue, or lag queries120% slowerQuerying historical data, e.g. +for as-of, issue, or lag queries120% slower
    - + +

    The largest factor in the query time improvements for latest data is a reduction in the frequency and severity of long-running queries. In v3, over seven hundred queries required over ten seconds to compute, and the longest-running query took twelve minutes; in v4 only three queries needed more than ten seconds and no query took more than twenty-one seconds.

    +
    +
    +++++++ - - - + + + + + + + + + + - - - + + + + + - - - + + + + + - - - + + + + + - - - + + + + + - - - - - - - - + + + + +
    Query time (seconds)LatestHistoricalQuery time +(seconds)LatestHistorical
    Percentilev3v4v3v4
    Percentilev3v40th (minimum)0.0050.0050.0050 .005
    0th (minimum)0.0050.00525th0.0070.0060.0060.007
    25th0.0070.00650th (median)0.0090.0080.0070.008
    50th (median)0.0090.00875th0.0580.0170.0120.023
    75th0.0580.017
    100th (maximum)739.33220.069100th (maximum)739.33220.06917.12115.997
    -

    The largest factor in the query time improvements for latest data is a reduction in the frequency and severity of long-running queries. In v3, over seven hundred queries required over ten seconds to compute, and the longest-running query took twelve minutes; in v4 only three queries needed more than ten seconds and no query took more than twenty-one seconds.

    +

    For historical data, long-running queries were more frequent in v4 than in v3, but not more severe. In v3, only 8 queries required more than one second to compute, and the longest-running query took seventeen seconds; in v4 over two hundred queries needed more than one second, but the max query time was sixteen seconds.

    The empirical cumulative distribution functions for query times in the latest and historical datasets shows both of these effects in greater detail.

    - +
    +
    ---+++++ - - - + + + + + + + + + + - - - + + + + + - - - + + + + + - - - - - - - - + + + + +
    Long-running queries (counts)Latest (of 37,396)Historical (of 19,359)Long-running +queries (counts)Latest +(of 37,396)Historical +(of 19,359)
    Queries slower thanv3v4v3v4
    Queries slower thanv3v41 second2734178228
    1 second27341710 seconds706311
    10 seconds7063
    100 seconds30100 seconds3000
    +

    Fig 3: Empirical cumulative distribution functions for query times, latest data

    From 409237b98fd6f5a709a710585f1d513145392f22 Mon Sep 17 00:00:00 2001 From: Katie Mazaitis Date: Tue, 24 Jan 2023 16:42:39 -0500 Subject: [PATCH 14/20] Fix George's author blurb Co-authored-by: melange396 --- content/people/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/people/index.md b/content/people/index.md index 958f4aedc..332c040e4 100644 --- a/content/people/index.md +++ b/content/people/index.md @@ -265,7 +265,7 @@ people: team: - core - blog - description: specializes in Delphi's database and API, and is a Systems Software Engineer in the Machine Learning Department at CMU. + description: specializes in Delphi's database and API, and is a Software Engineer in the Machine Learning Department at CMU. - firstName: Robin lastName: Han image: robin-han-500x500-min.jpg From ceeb6ffb7e6f94e561537c5d95f1f1f98607f317 Mon Sep 17 00:00:00 2001 From: minhkhul Date: Wed, 25 Jan 2023 09:38:35 -0500 Subject: [PATCH 15/20] fix linking to COVIDcast Metadata --- content/blog/2022-12-01-epidata-v4.Rmd | 2 +- content/blog/2022-12-01-epidata-v4.html | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index be63eb6fe..c070c5d24 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -34,7 +34,7 @@ This work was driven by the necessity to fix several specific deficiencies in th + Some data sources experience heavy revisions. The design of the v3 system required the database to scan all available issues of each matching reference date before it could identify the latest issue. The more revisions it needed to discard, the more time needed for the scan. * COVIDcast Metadata: - + We compile a list of all COVIDcast-related sources and signals available in the API, along with basic summary statistics such as the dates they are available, their minimum and maximum values, and the geographic levels at which they are reported. This is made available here. + + We compile a list of all COVIDcast-related sources and signals available in the API, along with basic summary statistics such as the dates they are available, their minimum and maximum values, and the geographic levels at which they are reported. This is made available [here](https://api.covidcast.cmu.edu/epidata/covidcast/meta). + The query to produce these aggregations was expensive in v3, requiring nearly five hours to run. Ideally we would like to update the metadata each hour when new data is added to the system. ### 2. Data Ingestion/Repair and Development diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html index ae6116876..bdfccc51c 100644 --- a/content/blog/2022-12-01-epidata-v4.html +++ b/content/blog/2022-12-01-epidata-v4.html @@ -50,7 +50,7 @@

    1. Customer Affecting

  • COVIDcast Metadata:
      -
    • We compile a list of all COVIDcast-related sources and signals available in the API, along with basic summary statistics such as the dates they are available, their minimum and maximum values, and the geographic levels at which they are reported. This is made available here.
      • +
    • We compile a list of all COVIDcast-related sources and signals available in the API, along with basic summary statistics such as the dates they are available, their minimum and maximum values, and the geographic levels at which they are reported. This is made available here.
    • The query to produce these aggregations was expensive in v3, requiring nearly five hours to run. Ideally we would like to update the metadata each hour when new data is added to the system.
    • From 9131a258ad3543505fba894441b815e24c91965d Mon Sep 17 00:00:00 2001 From: minhkhul Date: Wed, 25 Jan 2023 09:51:35 -0500 Subject: [PATCH 16/20] make is_latest fixed-width font and fix System design section format --- content/blog/2022-12-01-epidata-v4.Rmd | 11 ++++++++--- content/blog/2022-12-01-epidata-v4.html | 20 +++++++++++--------- 2 files changed, 19 insertions(+), 12 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index c070c5d24..9d65e3573 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -39,8 +39,8 @@ This work was driven by the necessity to fix several specific deficiencies in th ### 2. Data Ingestion/Repair and Development -* Is_latest column: - + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. +* ``is_latest`` column: + + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. * Reduce repetition/normalize + The v3 table included the full text of the source, signal, geo type, and geo value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo type, geo value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. @@ -52,10 +52,15 @@ This work was driven by the necessity to fix several specific deficiencies in th ## System design Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: + * The data source and signal, specifying the providing authority ("JHU" for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). + * The geographic type and a specific location (such as "state" & "Pennsylvania" or "county" & "Allegheny"). + * The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. + * An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). + * The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error). A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent "issue" for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the "latest issue" of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming. @@ -68,7 +73,7 @@ We chose to switch from [MariaDB](https://mariadb.org) to [Percona Server for My To reduce our data footprint and speed lookups, we took steps to "[normalize](https://en.wikipedia.org/wiki/Database_normalization)" our database. This involved redefining our schema to move some columns out of the main table and into tables of their own, which we reference with "[foreign key](https://en.wikipedia.org/wiki/Foreign_key)" columns instead. By arranging our data this way, the long and often-repeated text strings that name our sources and signals as well as the geographic locations we cover are only stored once each, and referred to by a number instead. The relatively short numbers take up much less space on disk compared to the text that they replace, and the database can optimize locating records by these numbers more easily than it can for text. We employ two of these dimension tables, one for source and signal, and one for geographic type and value. Using dimension tables efficiently requires [JOIN](https://en.wikipedia.org/wiki/Join_(SQL)) operations when retrieving from the database. We avoided adding these slightly-more-complicated JOIN statements to many places in our code base by hiding them behind [VIEWs](https://en.wikipedia.org/wiki/View_(SQL)) -- our VIEWs are a shortcut to the JOIN logic that make our new normalized tables look virtually indistinguishable from old monolithic table to our web server code. -We also added a "latest" table that includes a copy of just the issues that are most recent for all of our data points and removed the "latest issue" flag. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main [website](https://delphi.cmu.edu/covidcast/), so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The "latest table" simplifies our data management responsibilities compared to the "latest flag", in that we don't have to clear a flag from an old row (a previously "latest" issue of data) when a new "latest" row is added -- instead we simply overwrite the old row in our latest table, and the database does this with great ease. +We also added a "latest" table that includes a copy of just the issues that are most recent for all of our data points and removed the latest issue flag ``is_latest``. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main [website](https://delphi.cmu.edu/covidcast/), so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The "latest table" simplifies our data management responsibilities compared to the "latest flag", in that we don't have to clear a flag from an old row (a previously "latest" issue of data) when a new "latest" row is added -- instead we simply overwrite the old row in our latest table, and the database does this with great ease. Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more [indexes](https://en.wikipedia.org/wiki/Database_index) in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast. diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html index bdfccc51c..f5d3f68bc 100644 --- a/content/blog/2022-12-01-epidata-v4.html +++ b/content/blog/2022-12-01-epidata-v4.html @@ -58,9 +58,9 @@

    1. Customer Affecting

    2. Data Ingestion/Repair and Development

      -
    • Is_latest column: +
    • is_latest column:
        -
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
        • +
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
    • Reduce repetition/normalize
        @@ -80,12 +80,14 @@

        3. Systems Maintenance

    System design

    -

    Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being: -* The data source and signal, specifying the providing authority (“JHU” for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients). -* The geographic type and a specific location (such as “state” & “Pennsylvania” or “county” & “Allegheny”). -* The time type (either day or week) along with an actual numeric for the time value that the data corresponds to. -* An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected). -* The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error).

    +

    Our initial v3 schema was based on a single-table design. This worked well for a time. Each row represented a data point in its entirety, the most significant being:

    +
      +

    • The data source and signal, specifying the providing authority (“JHU” for example) and the type of data (like number of people vaccinated as a percent of the population, or the number of hospital beds occupied by covid patients).

      • +

    • The geographic type and a specific location (such as “state” & “Pennsylvania” or “county” & “Allegheny”).

      • +

    • The time type (either day or week) along with an actual numeric for the time value that the data corresponds to.

      • +

    • An issue date (the day this data was provided to us, as data is often re-issued to provide updates when entities may be slow to report, or when errors are corrected).

      • +

    • The value of the data itself (typically a number representing a count or percentage, which also includes (when available) sample size and standard error).

      • +

    A small single-table design can be manageable, but our data set grew very quickly and challenges appeared. In particular, it became more difficult for the system to locate the most recent “issue” for each data point. Accessing such data is especially pertinent because it represents the most recent and accurate information that we have, and it accounts for 65 percent of the requests we receive. To ameliorate performance degradation, we added an additional column to flag the “latest issue” of data for each source, signal, time, and location. This helped but still left lingering concerns about speed. Also, adding this flag meant that we had to make sure it was appropriately updated whenever new data was issued. We had a few unfortunate events where this flag was not updated correctly, and the remedy can be quite time consuming.

    @@ -94,7 +96,7 @@

    System design

    Eventually we undertook the challenge of trying to improve our performance and reduce our resource usage – to come up with v4!

    We chose to switch from MariaDB to Percona Server for MySQL for our database engine and management system. Both are variants of the free open source MySQL database, and both provide additional support and features on top of the base product. Percona offered us access to a more consistently developed drop-in replacement for MySQL, along with expertise and knowledge concentrated in both the open source community and Percona the company. Additionally, Percona Server for MySQL is a reliable target for monitoring with Percona’s PMM.

    To reduce our data footprint and speed lookups, we took steps to “normalize” our database. This involved redefining our schema to move some columns out of the main table and into tables of their own, which we reference with “foreign key” columns instead. By arranging our data this way, the long and often-repeated text strings that name our sources and signals as well as the geographic locations we cover are only stored once each, and referred to by a number instead. The relatively short numbers take up much less space on disk compared to the text that they replace, and the database can optimize locating records by these numbers more easily than it can for text. We employ two of these dimension tables, one for source and signal, and one for geographic type and value. Using dimension tables efficiently requires JOIN operations when retrieving from the database. We avoided adding these slightly-more-complicated JOIN statements to many places in our code base by hiding them behind VIEWs – our VIEWs are a shortcut to the JOIN logic that make our new normalized tables look virtually indistinguishable from old monolithic table to our web server code.

    -

    We also added a “latest” table that includes a copy of just the issues that are most recent for all of our data points and removed the “latest issue” flag. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main website, so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The “latest table” simplifies our data management responsibilities compared to the “latest flag”, in that we don’t have to clear a flag from an old row (a previously “latest” issue of data) when a new “latest” row is added – instead we simply overwrite the old row in our latest table, and the database does this with great ease.

    +

    We also added a “latest” table that includes a copy of just the issues that are most recent for all of our data points and removed the latest issue flag is_latest. Since this no longer relies on looking in our gigantic collection of all issues for data that is flagged as being the latest, we gained a significant improvement to query times. This is the data that drives our main website, so it is important for these operations to be fast for those browsing, and since we have so many visitors, it adds up to reduce our load significantly. The “latest table” simplifies our data management responsibilities compared to the “latest flag”, in that we don’t have to clear a flag from an old row (a previously “latest” issue of data) when a new “latest” row is added – instead we simply overwrite the old row in our latest table, and the database does this with great ease.

    Additionally, the reduced size of the foreign keys compared to their text equivalents made it possible to include more indexes in our database that let it quickly find data based on different subsets of criteria. No matter how our users specify their requests, we have ways to look it up fast.

    From 44541e2709cd6eb6d75657726e9faeba4d5487f2 Mon Sep 17 00:00:00 2001 From: george haff Date: Wed, 25 Jan 2023 12:14:49 -0500 Subject: [PATCH 17/20] use fixed-width for all explicit column name references --- content/blog/2022-12-01-epidata-v4.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 9d65e3573..5cb6a7ac5 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -28,7 +28,7 @@ This work was driven by the necessity to fix several specific deficiencies in th ### 1. Customer Affecting * Slow analytics queries: - + Computing any kind of aggregate summaries in v3 was excessively expensive, with queries frequently requiring hours or even days to complete. Aggregate statistics and other analytics queries are needed in order to help determine the scope of outages (How many rows were added during the period when the bad code was live, and what range of time_values was affected?) as well as quantify the impact of our dataset for project reports and proposals (How much data do we have from each of our different data sources? What is typical latency? How does latency behavior differ for different types of data sources?). + + Computing any kind of aggregate summaries in v3 was excessively expensive, with queries frequently requiring hours or even days to complete. Aggregate statistics and other analytics queries are needed in order to help determine the scope of outages (How many rows were added during the period when the bad code was live, and what range of time values were affected?) as well as quantify the impact of our dataset for project reports and proposals (How much data do we have from each of our different data sources? What is typical latency? How does latency behavior differ for different types of data sources?). * Slow latest queries: + Some data sources experience heavy revisions. The design of the v3 system required the database to scan all available issues of each matching reference date before it could identify the latest issue. The more revisions it needed to discard, the more time needed for the scan. @@ -40,10 +40,10 @@ This work was driven by the necessity to fix several specific deficiencies in th ### 2. Data Ingestion/Repair and Development * ``is_latest`` column: - + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. + + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each ``(source, signal, time_type, time_value, geo_type, geo_value)`` tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. * Reduce repetition/normalize - + The v3 table included the full text of the source, signal, geo type, and geo value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo type, geo value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. + + The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same ``(source, signal, geo_type, geo_value)``. Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. ### 3. Systems Maintenance From 4c0be923412517869b6503845632bda60e9079f7 Mon Sep 17 00:00:00 2001 From: minhkhul Date: Wed, 25 Jan 2023 21:19:47 -0500 Subject: [PATCH 18/20] generated html with fixed-width font for all explicit column name references --- content/blog/2022-12-01-epidata-v4.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html index f5d3f68bc..83af1a4ad 100644 --- a/content/blog/2022-12-01-epidata-v4.html +++ b/content/blog/2022-12-01-epidata-v4.html @@ -42,7 +42,7 @@

    1. Customer Affecting

    • Slow analytics queries:
        -
      • Computing any kind of aggregate summaries in v3 was excessively expensive, with queries frequently requiring hours or even days to complete. Aggregate statistics and other analytics queries are needed in order to help determine the scope of outages (How many rows were added during the period when the bad code was live, and what range of time_values was affected?) as well as quantify the impact of our dataset for project reports and proposals (How much data do we have from each of our different data sources? What is typical latency? How does latency behavior differ for different types of data sources?).
        • +
      • Computing any kind of aggregate summaries in v3 was excessively expensive, with queries frequently requiring hours or even days to complete. Aggregate statistics and other analytics queries are needed in order to help determine the scope of outages (How many rows were added during the period when the bad code was live, and what range of time values were affected?) as well as quantify the impact of our dataset for project reports and proposals (How much data do we have from each of our different data sources? What is typical latency? How does latency behavior differ for different types of data sources?).
    • Slow latest queries:
        @@ -60,11 +60,11 @@

        2. Data Ingestion/Repair and Development

        • is_latest column:
            -
          • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
            • +
          • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
        • Reduce repetition/normalize
            -
          • The v3 table included the full text of the source, signal, geo type, and geo value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo type, geo value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.
            • +
          • The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo_type, geo_value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.
    From c13a74d95053efe77a0de8631d66bb5a7f600c79 Mon Sep 17 00:00:00 2001 From: george haff Date: Thu, 26 Jan 2023 12:21:25 -0500 Subject: [PATCH 19/20] split up fixed-width sections to avoid non-blocking spaces --- content/blog/2022-12-01-epidata-v4.Rmd | 4 ++-- content/blog/2022-12-01-epidata-v4.html | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index 5cb6a7ac5..e34fe2177 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -40,10 +40,10 @@ This work was driven by the necessity to fix several specific deficiencies in th ### 2. Data Ingestion/Repair and Development * ``is_latest`` column: - + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each ``(source, signal, time_type, time_value, geo_type, geo_value)`` tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. + + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each ``(source,`` ``signal,`` ``time_type,`` ``time_value,`` ``geo_type,`` ``geo_value)`` tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. * Reduce repetition/normalize - + The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same ``(source, signal, geo_type, geo_value)``. Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. + + The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same ``(source,`` ``signal,`` ``geo_type,`` ``geo_value)``. Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. ### 3. Systems Maintenance diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html index 83af1a4ad..a8171b402 100644 --- a/content/blog/2022-12-01-epidata-v4.html +++ b/content/blog/2022-12-01-epidata-v4.html @@ -60,11 +60,11 @@

    2. Data Ingestion/Repair and Development

    • is_latest column:
        -
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
        • +
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
    • Reduce repetition/normalize
        -
      • The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo_type, geo_value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.
        • +
      • The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo_type, geo_value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.
    From 6985914005cb2e1d0704d21cc27e90a64e680266 Mon Sep 17 00:00:00 2001 From: minhkhul Date: Thu, 26 Jan 2023 12:51:52 -0500 Subject: [PATCH 20/20] final cleanup --- content/blog/2022-12-01-epidata-v4.Rmd | 4 ++-- content/blog/2022-12-01-epidata-v4.html | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/content/blog/2022-12-01-epidata-v4.Rmd b/content/blog/2022-12-01-epidata-v4.Rmd index e34fe2177..78b4d3d4e 100644 --- a/content/blog/2022-12-01-epidata-v4.Rmd +++ b/content/blog/2022-12-01-epidata-v4.Rmd @@ -40,10 +40,10 @@ This work was driven by the necessity to fix several specific deficiencies in th ### 2. Data Ingestion/Repair and Development * ``is_latest`` column: - + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each ``(source,`` ``signal,`` ``time_type,`` ``time_value,`` ``geo_type,`` ``geo_value)`` tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. + + Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (``source``, ``signal``, ``time_type``, ``time_value``, ``geo_type``, ``geo_value``) tuple had the ``is_latest`` flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive. * Reduce repetition/normalize - + The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same ``(source,`` ``signal,`` ``geo_type,`` ``geo_value)``. Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. + + The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same (``source``, ``signal``, ``geo_type``, ``geo_value``). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset. ### 3. Systems Maintenance diff --git a/content/blog/2022-12-01-epidata-v4.html b/content/blog/2022-12-01-epidata-v4.html index a8171b402..f75bec5ce 100644 --- a/content/blog/2022-12-01-epidata-v4.html +++ b/content/blog/2022-12-01-epidata-v4.html @@ -60,11 +60,11 @@

    2. Data Ingestion/Repair and Development

    • is_latest column:
        -
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
        • +
      • Because v3 recorded which row represented the most up-to-date value among a set of revisions using a flag, this flag had to be meticulously maintained to ensure that exactly one row for each (source, signal, time_type, time_value, geo_type, geo_value) tuple had the is_latest flag set. This flag had to be unset when newer data was added, but left in place when older data was patched. System faults while data was being loaded could leave the records in an inconsistent state where two or more, zero, or the wrong rows had the flag set. Detecting problems with this flag was computationally expensive.
    • Reduce repetition/normalize
        -
      • The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo_type, geo_value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.
        • +
      • The v3 table included the full text of the source, signal, geographic type, and geographic value for each row, such that we were storing millions of copies of identical information for rows that shared the same (source, signal, geo_type, geo_value). Since the duplicated information was also the most common selection criteria, the indexes took up a disproportionate amount of space on disk relative to the size of the base dataset.