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b/sql-tuning-best-practice.md new file mode 100644 index 0000000000000..56b0bb654fa30 --- /dev/null +++ b/sql-tuning-best-practice.md @@ -0,0 +1,204 @@ +--- +title: SQL Tuning Best Practice +summary: Learn how to do SQL tuning in TiDB + +--- + +# Introduction to SQL Tuning + +SQL tuning is a critical aspect of optimizing database system performance. It involves a systematic approach to improve the efficiency of SQL queries. The process typically consists of three key steps: + +1. Identify high-impact SQL statements: + - Review SQL execution history to find statements that consume a large portion of system resources or contribute significantly to the application workload. + - Use monitoring tools and performance metrics to pinpoint these resource-intensive queries. + +2. Analyze execution plans: + - Examine the execution plans generated by the query optimizer for the identified statements. + - Verify that these plans are reasonably efficient and utilize appropriate indexes and join methods. + +3. Implement optimizations: + - Apply corrective actions to improve poorly performing SQL statements. + - This may include rewriting queries, adding or modifying indexes, updating statistics, or adjusting database parameters. + +These steps are iteratively repeated until: +- The system performance meets the desired targets, or +- No further improvements can be made to the remaining statements. + +It's important to note that SQL tuning is an ongoing process. As your data volumes grow and query patterns evolve, you should: +- Regularly monitor query performance +- Re-evaluate your optimization strategies +- Adapt your approach to address new performance challenges + +By consistently applying these practices, you can ensure that your database maintains optimal performance over time. + +# Goals for Tuning + +The primary objectives of SQL tuning are to: + +1. Reduce response time for end users +2. Minimize resource consumption for processing workloads + +These goals can be achieved through various strategies: + +## Optimize Query Execution + +SQL tuning often involves finding more efficient ways to process the same workload without changing the query's functionality. This can be done by: + +1. Improving execution plans: + - Analyze and modify query structures to enable more efficient processing + - Utilize appropriate indexes to reduce data access and processing time + - Enable TiFlash for analytical queries on large volumes of data and Leverage MPP (Massively Parallel Processing) engine for complex aggregations and joins + +2. Enhancing data access methods: + - Use covering indexes to satisfy queries directly from the index, avoiding table access + - Implement partitioning strategies to limit data scans to relevant partitions + +Examples: + +- Creating an index for frequently queried columns can significantly reduce resource usage, especially for queries that access a small percentage of table data. +- Utilizing index-only scans for queries that return a limited number of sorted results can avoid full table scans and sorting operations. + +## Balance Workload Distribution + +In TiDB's distributed architecture, ensuring even workload distribution across multiple TiKV nodes is crucial for optimal performance: + +- Prevent hotspots: Design schemas and queries to avoid concentrated read or write operations on specific nodes +- Utilize parallel processing: + - Design queries to take advantage of TiDB's distributed execution capabilities + - Enable and tune parallel execution settings for resource-intensive operations + +By implementing these strategies, you can ensure that your TiDB cluster efficiently utilizes all available resources and avoids bottlenecks caused by uneven workload distribution or serialization on individual TiKV nodes. + +# Identifying High-Load SQL +The most efficient way to identify Resource-Intensive SQL is using TiDB Dashboard, There are other tools like views and logs available as well. + +## Monitoring SQL Statements by Using TiDB Dashboard +### SQL Statements Panel Default Display +In TiDB Dashboard, navigate to SQL Statements panel, which helps us identify the following: + +1. The SQL statement with the highest total latency is the one that takes the longest time to execute out of multiple executions of the same SQL statement. +2. It can also display the number of times each SQL statement has been executed cumulatively, allowing us to find the SQL statement with the highest execution frequency. +3. Clicking on each SQL statement allows us to delve deeper into the EXPLAIN ANALYZE results. + + + +SQL statements with the highest total and mean latency +SQL statements with the highest execution frequency +SQL statements are normalized as templates, literals and bind variables are replaced by ? + + +### Slow Queries Panel Default Display +In the TiDB Dashboard, we can find the Slow Query panel, which displays all SQL statements whose execution time exceeds the time threshold set by the system variable tidb_slow_log_threshold (default 300 milliseconds). + +On Slow Queries panel, we can find: + +1. The slowest SQL queries. +2. The SQL query that reads the most data from TiKV. +3. The EXPLAIN ANALYZE output from drilling down the SQL statement by clicking it. +4. Please note that on the Slow Queries panel, we cannot get the frequency of the SQL statement execution. Once the execution elapsed time exceeds tidb_slow_log_threshold for single instance, the query is then listed on the Slow Queries panel. + +## other tools for identify top sql +- [slow query log](https://docs.pingcap.com/tidb/stable/identify-slow-queries) +- [statements_summary view](https://docs.pingcap.com/tidb/stable/statement-summary-tables#statements_summary) +- [top sql feature](https://docs.pingcap.com/tidb/stable/top-sql) +- [tidb log](https://docs.pingcap.com/tidb/stable/identify-expensive-queries) +- [cluster_processlist view](https://docs.pingcap.com/tidb/stable/information-schema-processlist#cluster_processlist) + + +# SQL Tuning Guide + +This guide focuses on providing actionable advice for beginners looking to optimize their SQL queries in TiDB. By following these best practices, you can ensure better query performance and SQL Tuning. We'll cover below topic: +- Query Processing Workflow +- Optimizer Fundamentals +- Statistics Management +- Understand Execution Plan +- Real-World Use Case + - Bad Plan Debug + - Index Strategy in TiDB + - Partition table: local index vs global index + - Index Full Scan is fater than Table Full Scan + +# 1. Query Processing Workflow +the client sends a SQL statement to the protocol layer of TiDB server. The protocol layer is responsible for handling the connection between TiDB Server and the client, receiving SQL statement from the client, and returning data to the client. + +To the right of the protocol layer is the Optimizer layer of TiDB Server, which is responsible for processing SQL statements. The process is as follows: + +1. SQL statement arrives at the SQL optimizer through the protocol layer and is first parsed into an Abstract Syntax Tree (AST). +2. Pre-Process is primarily for Point Get. If it is a Point Get, the following-up optimization processes can be skipped, the next step jumps to the SQL Executor. +3. After confirming that it is not a Point Get, the AST goes to the Logical Transformation, which rewrites the SQL logically based on certain rules. +4. The AST that has gone through Logical Transformation will undergo Cost-Based Optimization. +5. During Cost-Based Optimization, the optimizer considers statistics to determine how to select specific operators and finally generates a physically executable physical execution plan. +6. The generated physical execution plan is sent to the SQL Executor of the TiDB database for execution. +7. Unlike traditional databases, TiDB databases need to push down the execution plan to different TiKV for reading and writing data. + +![workflow](/media/sql-tuning/workflow.png) + +# 1. Optimizer Fundamentals + +TiDB uses a cost-based optimizer (CBO) to determine the most efficient execution plan for a SQL statement. This optimizer evaluates different execution strategies and chooses the one with the lowest estimated cost. The cost is influenced by factors such as: + +- SQL +- Schema Design +- Statistics + - Table + - Index + - Column + +Based on the input, The cost model will produce the execution plan, which includes the details how the system execute the sql, including +- Access Method +- Join Method +- Join Order + +Best Practice: The optimizer is as good as the information it receives. Therefore, ensuring up-to-date statistics and well-designed indexes is critical. + +# How TiDB build A Execution Plan +## + +# 2. Statistics Management + +Statistics are essential to the TiDB optimizer. They include information about table data, such as the number of rows and distribution of values in columns. If statistics are outdated or inaccurate, the optimizer might generate suboptimal execution plans. + +# 3. Understanding Execution Plans +The execution plan represents the steps TiDB will follow to execute a SQL query. An effective execution plan ensures performance optimization. + + +# 4. Real-World Use Cases + +## Debugging a Bad Plan +When a SQL query performs poorly, it’s usually due to an inefficient execution plan. Key steps to debug: + +Compare estimated rows and actual rows from the execution plan. Large discrepancies indicate a problem with the statistics or index selection. +Ensure appropriate indexes are being used. If the query scans large portions of data, it may require better indexing or statistics updates. + +## Index Strategy in TiDB +Indexes play a critical role in query performance. A well-designed index can drastically reduce query execution time. + +Make sure to follow the composite index strategy +- Key predicates for index prefix columns, predicates on columns that data can be accessed directly: +- Columns After Index Prefix, leverage index to preprocess the sorting +- Additional Columns for Filtering, to reduce row lookups + +## Partition Tables: Local vs Global Indexes +Partitioning large tables can improve query performance, but the choice between local and global indexes is crucial. + +- Local Index: Indexed data is limited to a partition, leading to efficient access within that partition. +- Global Index: Spanning across partitions, global indexes help avoid partition lookups. +Best Practice: Use global indexes for queries accessing multiple partitions to reduce lookup overhead. + +## Index Full Scan vs Table Full Scan +In most cases, Index Full Scans are much faster than Table Full Scans because indexes are smaller and ordered. Use index full scans to minimize data access time. + +Best Practice: + +Ensure that queries make use of index scans whenever possible. A full table scan should only happen when absolutely necessary. + + +# Key Takeaways +- The quality of the execution plan is dependent on the quality of the inputs. Ensure statistics are up to date and reflect the data accurately. +- Compare estimated rows and actual rows to verify that the optimizer has accurate statistics. +- Follow the composite index strategy: + - Use key predicates for index prefix columns. + - Leverage columns after the index prefix to support sorting and filtering. + . - Add extra columns to reduce row lookups. +- Choose between global and local indexes for partitioned tables based on the query access pattern. Use global indexes to avoid lookup overhead across partitions. + From 1edd2743ebed543d0eaa54864296c83b611f521c Mon Sep 17 00:00:00 2001 From: dbsid Date: Mon, 14 Oct 2024 11:44:33 +0800 Subject: [PATCH 02/46] draft 2 --- media/sql-tuning/slow-query-default.png | Bin 0 -> 773532 bytes media/sql-tuning/slow-query-dimension.png | Bin 0 -> 1064851 bytes media/sql-tuning/sql-statement-dimension.png | Bin 0 -> 1151930 bytes media/sql-tuning/sql-statements-default.png | Bin 0 -> 536272 bytes sql-tuning-best-practice.md | 83 +++++++++++++++---- 5 files changed, 68 insertions(+), 15 deletions(-) create mode 100644 media/sql-tuning/slow-query-default.png create mode 100644 media/sql-tuning/slow-query-dimension.png create mode 100644 media/sql-tuning/sql-statement-dimension.png create mode 100644 media/sql-tuning/sql-statements-default.png diff --git a/media/sql-tuning/slow-query-default.png b/media/sql-tuning/slow-query-default.png new file mode 100644 index 0000000000000000000000000000000000000000..23aaf26cec2cccc2a119dc4bf5199a42bfd498c2 GIT binary patch literal 773532 zcmdqIX*iVc8#gXXvXdD5Bt;RTvJNWAS29z{QjEbQk$q>3L1d?rkTH^KC~H}>jD6q7 zGK_s+#xjh-4CC>iB} z-@mAA-`}?RVnpj*c-VuB%|f;wEHZY|!4Y8%DHk_cIb1*z*9)0IHypVy_XclOkF0Ih zw@1fk#drU*L1Z=AeIg^bY$nZ~LmoWP6%d7{r?9z<{c-5}tY}sK|EtN#3boI{A#|4$2c_GK=g9KlX@ijV^Jk9Yx0AWS@JCkZu&qb;APFb1Y;-Uce4`QH{W zY$8It7Q;_yxD(j+)=>}7?<-8peoPk|^=KLXM;WMHM#im)|Lq5KN|7PL61CSJLN*V5 zK}|mS-}_9DJI{|%%Vx?yUA_1!H(W6-&w*r{`+8l)D}gFD|Cx>MEL2`1~kA)oOb&I&RBvb-akr z9Q|w5<;H)FeTg&DcTDPk)#9VUqPF*oZuiKeGo+i)QJ#V<<-<<=)>Nff&BU(*TO0PO z%HQg~bS~XT+pLk2G$%j7{u8+`h`OdR#%FQ7P7#L1&lRVC)AXhv-;RxYH-;35-7W3H z>}13HkRKM5qr4V8COqwD0*%VM)UcG8`lAcl8X=%e#fOs<%M!5Q=g5st{)REeYZ!Oq zqG2exP4YmQF~)bae8T8snvY9@`qlip1RK4^pZn1RtTd8ajId0*9&2}f4Qh)(tXDn= zqbq&Kv``ECmOgeqfxW-|N~zdUtEl9-trO5^7bQ4_WTD1@bv`~ho_>3L343NVO2I0k z2&K;O8tH8#%v(~3C6ksu@9=+bqP3(C=8RCH@&Yw^xKBYq<2=1@wJt90VaIHB&n&7` zC|7j40%$tmCE zanj;_2ny17lU%wlW7Fg}jaA`W*2wHT$TmVQS2I2;Jg0Tt!hmPN3&6a)8%^20H%Uy( zIBu1#7Eh#k(aJFtwYo}IZZ$>q9HMEX#}3h0mWBN}r!kzehnC0hg~^oy`94S?2e1FF0Ofm4pZsIh#oswD|E9e9HaYKwS-*Ly?#1lx0<*u$gO~&pKt^ELy`~{zn2cg;H9*i4GuT;{#xhw z$0DmQM9t4KW_M#wDANe8r7X^>fA>r^?g-{8_j8&6$FoRl#3@L{jxNtvN>2EFbt#4Q zpk&N?xmMI<*?LD~`-Ulj!J8PBdN8QMTy8AQTrOq*ckek#K5Sb(j};KKm=DZbX?vX2 zR_GdJ)15ju$MuW2D_;&Yd}nFW&|4txto{34&So3qN$>Eq5Z7^=bi|an0f93n~!Ao&g~{Knjb< zPDYA|S$U2l(Fex$ypy26e_QdI`}u(_A~PPH0c)sj&-{w%%iSu{YhC5f2qwqwOG7-&GNZA>>DF*j2;`s()OjU%OF0I=!O!DYIPdf)MoN1- z^$V~H88P!#5FDk#^kFp9|A(?HicE%wXPS{Ue&80q&HieyH7f+T!oJpl&Wgm|&Y178 zO@f2HJ-h2Xwkr5zMX<%y_cDQ1dcP#Hk)mD^@=GS(&QVYgEn%xRQ*GyxQlR(_U)H`e z(GVs2rzII)U$iuISD0MdXp?kixwv*}J)mQAk=JzBf+6nf3^MYHJzLgxTk_uh@pKvKB>-A=k&$uCP#>(BU`tu-@u z+i`dMIv-CEV|K}-bCl4G+>e^ab~(cuKx<|<;U6xeo$g=JaHbVMiw2e2z$tjVsuG?O@z7Qzg|LjT3&bj@Q+EznJ>ad$=PXKy!21EAWYcY_e#IbkP~| z?5{PwwLaxbue#d-#CZ=DeOn0(X{q~Gc6{b+7w`GYsH{PlUYf@f7bZyF{&|fB`eXX< zl*4da<^cBDWK6BU(xb;MO5Ht6bJj_{4#%Fav$sd8!}_bpT-Gf)vP@U;;&xqlSL#vgMr^ zjeC_;ZLbY(43wG!<=o))2F1AOZD~;OXdQoJIgi>oNJ-SspunsMb#jXWOOo+!+Y+vz z*WaUp99c!SEIcNm6ZJ2Jk!5dBHe>=LT6Gf4+=l{!7yUMSdtg$7<(%|OT6*!XcsWDBBMHtY%kPQXJUeqzrt|u0#?R=4zf;~|oG_%M zxwr1Am2aa{hurIEtQpjC-X|U_AimkP4!=|B8oMsD19&4Mm|15JE`Wt`D#0#I| ztiGL-h}F4?v9-GHTQ@xa@snnp&ah|{dFY??eShsw!lVAE>=o_xV5lmeR@}x$a-X3U z<(Ew~Hp*cE-G^{ow_b+dd2TW&vk1g%=&*D|&5Q5{R_vrcX$@ElOf9i$IPjfQmR@?E zeufAY7+N9BX%{Z3V4m_RNMnA2`n(mBly|2q3Xx~DwT=$x!hVvMfeO_ry(eIj+O=Xq zXFWP+IWWi6vy)vFn_QrPeCO@JIJ3T!2tDLlQgL5t$`;_-Jk*UpG|bF4diNea73!+e zN1APAs?9x3r>y^_>aKAID`3HV(E%i3h-~VG_Io>nZ(Ed% z7nZ1c`0ad&PV!bAc=)SEeRRV|eA&etJEGYLD%}kL-SSr^8I|2Y7tU$#UyRBfzBjid9i3$sUqc&OYt6x;T-tT44F`(Nb>UkiS8?>Cw<-_Lsb zIgQLT-?>5U)=z|aIB}h0I#ST2axgWA_aHBs%7rR-AAI}izkd0hNqzY`M$I7!!q510 zcS-4cwV(%9E#^?Ep`hWM%3|AQvZ(untXEcZfDcJ+b)RDjFIc$mdRMt zL`l~D1J?P5c;FlW5){r6u)Gw}9f7;j3K&}#@cC9_B9;Sj2^R$dq6Kg2_OA_c%wbE9 zYtw~bObpIeD1|jNYNkAr-Szy7k`vaTkBmErf=2w_Y=SaUpc%)!&90)$WF37s5tv{U z!Cq#-1AET_y##6q=#21bT6#AbbT+GTRWBD=*XevqcAml(A;*QyBU6}XdVAW3GmyfOm;Bwoj}YUXkU5vz}~1Uw#8l(6+!PTaYN85_Xn@ z2CcgD3}B4O7qDs>oAjFU#Wj^J$UT@fneB7HIBk#5=+RUp>?HO}FK}r5m)68(hP53R zt4R5sMalSz2Qm|9f}J0H@zNRgJ(89_*<=0o@z(2yf7;mzYMCAE1UH}SC41u4zrUN( zIq#27y?>dIRV!PbqpY14U!V^H5O@`SUY>o_Hqu2)y6Vq^_4@<0nmv5yzw7T3OGvZp zv&oc+;X*snuQjP5uB!Qx0)hXuD6QNQe#M4M?0f|=gqIEw&+)PZLohM|%ah?V zo&`Vp)8+(jV(=1DZ~@@m3q&_|Oaj(}@Bwd1x)EAX+ylF}#Y--oDg7HcuD{n#zD4{O zj}>(3(vv6cDQwR?^aIleCt**!6@p9Ru{xk!+{JAcc0x*~K1eZOwB$ITtAsC`V1~xf zjD_?r&2NH5)2h1d>Y72up}?ecEQa7p8?tq!Jptp8eMpGqNY;_if@QTL%14AsOl z%W#__nGCPkf-@&A=yC6$1D>_>TcFR@WY*$dAGK?m<~!8ev|jPkdOWDz#-r{6 z*_k@chzq!cv=8U<=;x#|NP0baNYKXcFk=qY)w0WhTPRI(fAtph*{Wt&t5TOjhQDu4 zvN$p*%3vr3UBQ%#A$*?dnrz8P*y6dx-|^(r`%~fs+(>$HcxBtJ<=bkX3Gk`7#LRgo z(bRu-d+XQ1S!BSp`h{X=(fPdAL=lL|vNu}ybIPmfsWVKWk2v+nwvE*;h}|$Xm3{So zY>)5Lrx>*;&_<`kCQL9?uWIA^j78LGSSF;2_%rDZ$evJQe4rBK80vRYBynMk$F+>@ zuBZ#?j04Zef=}uj0%rb>Dm3A0Zn!Sm{u@B(-V2$#c>jhZlCgwV`~zT6Y1x54eZ; 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Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. +TiDB is a distributed SQL database that completely decouples the SQL layer (TiDB) from the storage layer (TiKV). Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. Typical KV RPC are `Point_Get`, `Batch_Point_Get`, and Cop tasks. In TiDB, leveraging indexes effectively is crucial for performance tuning, as it can significantly reduce the number of KV RPC requests. By minimizing these requests, you can greatly improve query performance and overall system efficiency. Here are some key strategies: - Avoiding full table scans - Avoiding sorting -- Skipping row lookups when possible +- Skipping row lookups when possible by using covering index or not request non-needed columns -This section showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covered indexes for both filtering and sorting operations. +This section explains the general index strategy and the cost of index. It showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covering indexes for both filtering and sorting operations. -### SQL Tuning with a Covered Index +### Composite Index Strategy Guidelines + +When creating a composite index, it's essential to follow a specific order for the columns to ensure optimal performance. This order is crucial because it directly impacts how efficiently the index can filter and sort data. -A covered index is designed to include all columns referenced in the filter and select clauses. The query below requires an index lookup of 2597411 rows, taking 46.4 seconds to execute. TiDB needs to dispatch 67 cop tasks for the index range scan on logs_idx, identified as `IndexRangeScan_11`, and 301 cop tasks for table access via `TableRowIDScan_12`. By utilizing a covered index, the index lookup can be avoided, leading to improved performance. +Here is the recommended order for the columns in the index: + +1. Equal predicates for index prefix (columns accessed directly): + - Equal conditions + - IS NULL conditions + +2. Columns after index prefix for sorting: + - Leverage index to preprocess sorting + - Ensure sort and limit pushdown to TiKV + - Maintain sorted order + +3. Additional filtering columns, to reduce row lookups: + - Non-equal predicates (`!=`, `<>`, `IS NOT NULL`, etc) + - Time range conditions on datetime columns + - Helps reduce row lookups + +4. Columns in index postfix for select list or aggregattion, to avoid row lookups + +### The Cost of Indexing + +While indexes can significantly improve query performance, they also come with costs that should be carefully considered: + +1. Performance Impact on Writes: + - Non-clustered index decreases the chance of single phase commit optimization. + - Each additional index slows down write operations (INSERT, UPDATE, DELETE) + - When data is modified, all affected indexes must be updated + - This overhead increases with each additional index + +2. Resource Consumption: + - Indexes require additional disk space + - More memory is needed to cache frequently accessed indexes + - Backup and recovery operations take longer + +3. Write Hotspot Risk: + - Secondary indexes can create write hotspots, for example, a datetime monotonically increasing index will cause the hotspots on the write of the table + - Performance can degrade significantly if hotspots occur + +Best Practice: + +- Only create indexes that provide clear performance benefits +- Regularly review index usage statistics via [TIDB_INDEX_USAGE](/information-schema/information-schema-tidb-index-usage.md) +- Consider the write/read ratio of your workload when designing indexes + + +### SQL Tuning with a Covering Index + +A covering index is designed to include all columns referenced in the filter and select clauses. The query below requires an index lookup of 2597411 rows, taking 46.4 seconds to execute. TiDB needs to dispatch 67 cop tasks for the index range scan on logs_idx, identified as `IndexRangeScan_11`, and 301 cop tasks for table access via `TableRowIDScan_12`. By utilizing a covering index, the index lookup can be avoided, leading to improved performance. ```sql SELECT @@ -597,8 +645,11 @@ WHERE After creating the covered index below, which includes the additional columns `source_type`, `target_type`, and `amount`, the query execution time improved to 90ms, and TiDB only needed to send a single cop task to TiKV for data scanning. +Be noted after creating the index, the `ANALYZE TABLE` command is used to gather the statistics for the index. So that the optimizer can make the best use of the index. Since In TiDB, create index will not gather the statistics for the index. + ```sql CREATE INDEX logs_covered ON logs(snapshot_id, user_id, status, source_type, target_type, amount); +ANALYZE TABLE logs INDEX Logs_covered; ``` ``` @@ -617,18 +668,18 @@ CREATE INDEX logs_covered ON logs(snapshot_id, user_id, status, source_type, tar When optimizing SQL queries, especially those that include an `ORDER BY` clause, it is beneficial to create a composite index that encompasses both the filtering and sorting columns. This approach allows the database engine to efficiently access the required data while maintaining the desired order. -For instance, consider the following query that retrieves logs based on specific conditions. The execution plan shows a duration of 170ms. TiDB employs the `logs_index` to perform an `IndexRangeScan_20` with the filter `snapshot_id = 459840`. Subsequently, it retrieves all columns from the table, resulting in 5715 rows being streamed back to TiDB after the `IndexLookUp_23`, which then sorts the dataset and returns 1000 rows. Note that the `id` column is the primary key, which means it is implicitly included in the `logs_idx` index. However, for `IndexRangeScan_20`, the order is not guaranteed because, after the index prefix column `snapshot_id`, there are two additional columns: `user_id` and `status`. Therefore, the ordering of `id` cannot be assured. +For instance, consider the following query that retrieves data from `test` based on specific conditions. The execution plan shows a duration of 170ms. TiDB employs the `test_index` to perform an `IndexRangeScan_20` with the filter `snapshot_id = 459840`. Subsequently, it retrieves all columns from the table, resulting in 5715 rows being streamed back to TiDB after the `IndexLookUp_23`, which then sorts the dataset and returns 1000 rows. Note that the `id` column is the primary key, which means it is implicitly included in the `test_idx` index. However, for `IndexRangeScan_20`, the order is not guaranteed because, after the index prefix column `snapshot_id`, there are two additional columns: `user_id` and `status`. Therefore, the ordering of `id` cannot be assured. ```sql EXPLAIN ANALYZE SELECT -logs.* +test.* FROM - logs + test WHERE - logs.snapshot_id = 459840 - AND logs.id > 998464 + test.snapshot_id = 459840 + AND test.id > 998464 ORDER BY - logs.id ASC + test.id ASC LIMIT 1000 ``` @@ -639,19 +690,20 @@ Original Plan +------------------------------+---------+---------+-----------+----------------------------------------------------------+-----------------------------------------------+--------------------------------------------+ | id | estRows | actRows | task | access object | execution info | operator info | +------------------------------+---------+---------+-----------+----------------------------------------------------------+-----------------------------------------------+--------------------------------------------+ -| id | estRows | actRows | task | access object | execution info ...| logs.id, offset:0, count:1000 | +| id | estRows | actRows | task | access object | execution info ...| test.id, offset:0, count:1000 | | TopN_10 | 19.98 | 1000 | root | | time:170.6ms, loops:2 ...| | | └─IndexLookUp_23 | 19.98 | 5715 | root | | time:166.6ms, loops:7 ...| | -| ├─Selection_22(Build) | 19.98 | 5715 | cop[tikv] | | time:18.6ms, loops:9, cop_task: {num: 3, ...| gt(logs.id, 998464) | -| │ └─IndexRangeScan_20 | 433.47 | 7715 | cop[tikv] | table:logs, index:logs_idx(snapshot_id, user_id, status) | tikv_task:{proc max:4ms, min:4ms, avg: 4ms ...| range:[459840,459840], keep order:false | -| └─TableRowIDScan_21(Probe) | 19.98 | 5715 | cop[tikv] | table:logs | time:301.6ms, loops:10, cop_task: {num: 3, ...| keep order:false | +| ├─Selection_22(Build) | 19.98 | 5715 | cop[tikv] | | time:18.6ms, loops:9, cop_task: {num: 3, ...| gt(test.id, 998464) | +| │ └─IndexRangeScan_20 | 433.47 | 7715 | cop[tikv] | table:test, index:test_idx(snapshot_id, user_id, status) | tikv_task:{proc max:4ms, min:4ms, avg: 4ms ...| range:[459840,459840], keep order:false | +| └─TableRowIDScan_21(Probe) | 19.98 | 5715 | cop[tikv] | table:test | time:301.6ms, loops:10, cop_task: {num: 3, ...| keep order:false | +------------------------------+---------+---------+-----------+----------------------------------------------------------+-----------------------------------------------+--------------------------------------------+ ``` -To optimize the query, we can create a new index on (snapshot_id, id) to ensure that for each snapshot_id value, the id is sorted in the index. By utilizing this new index, the query execution time is reduced to 96ms. Note that the keep order is true for `IndexRangeScan_33`, and the `TopN` is replaced with `Limit`. For the `IndexLookUp_35`, only 1000 rows are returned to TiDB, eliminating the need for additional sorting operations. +To optimize the query, we can create a new index on `(snapshot_id, id)` to ensure that for each snapshot_id value, the id is sorted in the index. By utilizing this new index, the query execution time is reduced to 96ms. Note that the keep order is true for `IndexRangeScan_33`, and the `TopN` is replaced with `Limit`. For the `IndexLookUp_35`, only 1000 rows are returned to TiDB, eliminating the need for additional sorting operations. ```sql -CREATE INDEX logs_new ON logs(snapshot_id, id); +CREATE INDEX test_new ON test(snapshot_id, id); +ANALYZE TABLE test INDEX test_new; ``` New Plan @@ -662,16 +714,16 @@ New Plan +----------------------------------+---------+---------+-----------+----------------------------------------------+----------------------------------------------+----------------------------------------------------+ | Limit_14 | 17.59 | 1000 | root | | time:96.1ms, loops:2, RU:92.300155 | offset:0, count:1000 | | └─IndexLookUp_35 | 17.59 | 1000 | root | | time:96.1ms, loops:1, index_task: ...| | -| ├─IndexRangeScan_33(Build) | 17.59 | 5715 | cop[tikv] | table:logs, index:logs_new(snapshot_id, id) | time:7.25ms, loops:8, cop_task: {num: 3, ...| range:(459840 998464,459840 +inf], keep order:true | -| └─TableRowIDScan_34(Probe) | 17.59 | 5715 | cop[tikv] | table:logs | time:232.9ms, loops:9, cop_task: {num: 3, ...| keep order:false | +| ├─IndexRangeScan_33(Build) | 17.59 | 5715 | cop[tikv] | table:test, index:test_new(snapshot_id, id) | time:7.25ms, loops:8, cop_task: {num: 3, ...| range:(459840 998464,459840 +inf], keep order:true | +| └─TableRowIDScan_34(Probe) | 17.59 | 5715 | cop[tikv] | table:test | time:232.9ms, loops:9, cop_task: {num: 3, ...| keep order:false | +----------------------------------+---------+---------+-----------+----------------------------------------------+----------------------------------------------+----------------------------------------------------+ ``` ### SQL Tuning with Composite Indexes for Efficient Filtering and Sorting -The original query took 11 minutes and 9 seconds to complete, which is an extremely long execution time for a query that only needs to return 101 rows. This poor performance can be attributed to several factors: +The original query below took 11 minutes and 9 seconds to complete, which is an extremely long execution time for a query that only needs to return 101 rows. This poor performance can be attributed to several factors: -1. Inefficient index usage: The optimizer chose the index on created_at, which resulted in scanning 25,147,450 rows. +1. Inefficient index usage: The optimizer chose the index on `created_at`, which resulted in scanning 25,147,450 rows. 2. Large intermediate result set: After applying the date range filter, 12,082,311 rows still needed to be processed. 3. Late filtering: The most selective predicates (mode, user_id, and label_id) were applied after accessing the table, resulting in 16,604 rows. 4. Sorting overhead: The final sort operation on 16,604 rows added additional processing time. @@ -687,13 +739,14 @@ WHERE AND orders.label_id IS NOT NULL AND orders.created_at >= '2024-04-07 18:07:52' AND orders.created_at <= '2024-05-11 18:07:52' - AND id >= 1000000000 - AND id <= 2000000000 + AND orders.id >= 1000000000 AND orders.id < 1500000000 ORDER BY orders.id DESC LIMIT 101; ``` +Here is the index on `orders` + ```sql PRIMARY KEY (`id`), UNIQUE KEY `index_orders_on_adjustment_id` (`adjustment_id`), @@ -705,21 +758,21 @@ KEY `index_orders_on_created_at` (`created_at`) Original plan ``` -+--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ -| id | estRows | actRows | task | access object | execution info | operator info | memory | disk | -+--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ -| TopN_10 | 101.00 | 101 | root | | time:11m9.8s, loops:2 | orders.id:desc, offset:0, count:101 | 271 KB | N/A | -| └─IndexLookUp_39 | 173.83 | 16604 | root | | time:11m9.8s, loops:19, index_task: {total_time:...}| | 20.4 MB | N/A | -| ├─Selection_37(Build) | 8296.70 | 12082311| cop[tikv] | | time:26.4ms, loops:11834, cop_task: {num: 294, m...}| ge(orders.id, 1000000000), le(orders.id, 2000000000), lt(orders.id, 1500000000) | N/A | N/A | -| │ └─IndexRangeScan_35 | 6934161.90| 25147450| cop[tikv] | table:orders, index:index_orders_on_created_at(created_at) | tikv_task:{proc max:2.15s, min:0s, avg: 58.9ms, ...}| range:[2024-04-07 18:07:52,2024-05-11 18:07:52), keep order:false | N/A | N/A | -| └─Selection_38(Probe) | 173.83 | 16604 | cop[tikv] | | time:54m46.2s, loops:651, cop_task: {num: 1076, ...}| eq(orders.mode, "production"), eq(orders.user_id, 11111), not(isnull(orders.label_id)) | N/A | N/A | -| └─TableRowIDScan_36 | 8296.70 | 12082311| cop[tikv] | table:orders | tikv_task:{proc max:44.8s, min:0s, avg: 3.33s, p...}| keep order:false | N/A | N/A | -+--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ ++--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------+----------+------+ +| id | estRows | actRows | task | access object | execution info | operator info | memory | disk | ++--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------+----------+------+ +| TopN_10 | 101.00 | 101 | root | | time:11m9.8s, loops:2 | orders.id:desc, offset:0, count:101 | 271 KB | N/A | +| └─IndexLookUp_39 | 173.83 | 16604 | root | | time:11m9.8s, loops:19, index_task: {total_time:...}| | 20.4 MB | N/A | +| ├─Selection_37(Build) | 8296.70 | 12082311| cop[tikv] | | time:26.4ms, loops:11834, cop_task: {num: 294, m...}| ge(orders.id, 1000000000), lt(orders.id, 1500000000) | N/A | N/A | +| │ └─IndexRangeScan_35 | 6934161.90| 25147450| cop[tikv] | table:orders, index:index_orders_on_created_at(created_at) | tikv_task:{proc max:2.15s, min:0s, avg: 58.9ms, ...}| range:[2024-04-07 18:07:52,2024-05-11 18:07:52), keep order:false | N/A | N/A | +| └─Selection_38(Probe) | 173.83 | 16604 | cop[tikv] | | time:54m46.2s, loops:651, cop_task: {num: 1076, ...}| eq(orders.mode, "production"), eq(orders.user_id, 11111), not(isnull(orders.label_id)) | N/A | N/A | +| └─TableRowIDScan_36 | 8296.70 | 12082311| cop[tikv] | table:orders | tikv_task:{proc max:44.8s, min:0s, avg: 3.33s, p...}| keep order:false | N/A | N/A | ++--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------+----------+------+ ``` Performance Improvement with the New Index: -After creating the new index (new_idx on orders(user_id, mode, id, created_at, label_id)), the query performance improved dramatically. The execution time reduced from 11 minutes and 9 seconds to just 5.3 milliseconds, which is a staggering improvement of over 126,000 times faster. This massive improvement can be explained by: +After creating the new index `idx_composite` on `orders(user_id, mode, id, created_at, label_id)`, the query performance improved dramatically. The execution time reduced from 11 minutes and 9 seconds to just 5.3 milliseconds, which is a staggering improvement of over 126,000 times faster. This massive improvement can be explained by: 1. Efficient index usage: The new index allows for an index range scan on user_id, mode, and id, which are the most selective predicates. This drastically reduces the number of rows scanned from millions to just 224. 2. Index-only sort: The 'keep order:true' in the plan indicates that the index structure is used for sorting, avoiding a separate sort operation. @@ -728,7 +781,12 @@ After creating the new index (new_idx on orders(user_id, mode, id, created_at, l This case demonstrates the profound impact that a well-designed index can have on query performance. By aligning the index structure with the query's predicates, sort order, and required columns, we achieved a performance improvement of over five orders of magnitude. -Plan with new index (user_id, mode, id, created_at, label_id) +```sql +CREATE INDEX idx_composite ON Orders(user_id, mode, id, created_at, label_id); +ANALYZE TABLE orders index idx_composite; +``` + +Plan with new index `idx_composite` ``` +--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ @@ -737,59 +795,11 @@ Plan with new index (user_id, mode, id, created_at, label_id) | IndexLookUp_32 | 101.00 | 101 | root | | time:5.3ms, loops:2, RU:3.435006, index_task: {t...}| limit embedded(offset:0, count:101) | 128.5 KB | N/A | | ├─Limit_31(Build) | 101.00 | 101 | cop[tikv] | | time:1.35ms, loops:1, cop_task: {num: 1, max: 1....}| offset:0, count:101 | N/A | N/A | | │ └─Selection_30 | 535.77 | 224 | cop[tikv] | | tikv_task:{time:0s, loops:3} | ge(orders.created_at, 2024-04-07 18:07:52), le(orders.created_at, 2024-05-11 18:07:52), not(isnull(orders.label_id)) | N/A | N/A | -| │ └─IndexRangeScan_28 | 503893.42 | 224 | cop[tikv] | table:orders, index:index_orders_new(user_id, mode, id, created_at, label_id) | tikv_task:{time:0s, loops:3} | range:[11111 "production" 1000000000,11111 "production" 1500000000), keep order:true, desc | N/A | N/A | +| │ └─IndexRangeScan_28 | 503893.42 | 224 | cop[tikv] | table:orders, index:idx_composite(user_id, mode, id, created_at, label_id) | tikv_task:{time:0s, loops:3} | range:[11111 "production" 1000000000,11111 "production" 1500000000), keep order:true, desc | N/A | N/A | | └─TableRowIDScan_29(Probe) | 101.00 | 101 | cop[tikv] | table:orders | time:2.9ms, loops:2, cop_task: {num: 3, max: 2.7...}| keep order:false | N/A | N/A | +--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ ``` -### Composite Index Strategy Guidelines - -When creating a composite index, it's essential to follow a specific order for the columns to ensure optimal performance. This order is crucial because it directly impacts how efficiently the index can filter and sort data. - -Here is the recommended order for the columns in the index: - -1. Equal predicates for index prefix (columns accessed directly): - - Equal conditions - - IS NULL conditions - -2. Columns after index prefix for sorting: - - Leverage index to preprocess sorting - - Ensure sort and limit pushdown to TiKV - - Maintain sorted order - -3. Additional filtering columns: - - Non-equal predicates (`!=`, `<>`, `IS NOT NULL`, etc) - - Time range conditions on datetime columns - - Helps reduce row lookups - -4. columns in index postfix for select list or aggregate function: - - Leverage IndexReader - - Avoid IndexLookup operations - -### The Cost of Indexing - -While indexes can significantly improve query performance, they also come with costs that should be carefully considered: - -1. Performance Impact on Writes: - - Each additional index slows down write operations (INSERT, UPDATE, DELETE) - - When data is modified, all affected indexes must be updated - - This overhead increases with each additional index - -2. Resource Consumption: - - Indexes require additional disk space - - More memory is needed to cache frequently accessed indexes - - Backup and recovery operations take longer - -3. Write Hotspot Risk: - - Secondary indexes can create write hotspots, for example, a datetime monotonically increasing index will cause the hotspots on the write of the table - - Performance can degrade significantly if hotspots occur - -Best Practice: - -- Only create indexes that provide clear performance benefits -- Regularly review index usage statistics via [TIDB_INDEX_USAGE](/information-schema/information-schema-tidb-index-usage.md) -- Consider the write/read ratio of your workload when designing indexes - ## When to Use TiFlash: A Simple Guide This section explains the best scenarios for using TiFlash in TiDB. TiFlash is optimized for analytical queries that involve complex calculations, aggregations, and large dataset scans, thanks to its columnar storage format. Here’s when to consider TiFlash: @@ -797,7 +807,7 @@ This section explains the best scenarios for using TiFlash in TiDB. TiFlash is o - Large-Scale Data Analysis: For queries involving extensive data scans, such as OLAP workloads, TiFlash can deliver faster performance compared to TiKV due to its columnar storage and MPP capability. - Complex Scans, Aggregations and Joins: TiFlash is ideal for queries with heavy aggregations and joins, as it can process these operations more efficiently by only reading necessary columns. - Mixed Workloads: In hybrid environments where both transactional (OLTP) and analytical (OLAP) workloads run simultaneously, TiFlash can handle analytical queries without impacting TiKV’s performance for transactional queries. -- SaaS Arbitrary Filtering Workloads: In SaaS workloads, there is often a need for arbitrary filtering across many columns. Indexing all columns is impractical, especially when the primary key includes a tenant ID and all queries include this ID. TiFlash is an ideal solution here: because data in TiFlash is sorted and clustered by primary key, it enables efficient table range scans. This allows TiFlash to provide fast performance without the overhead of maintaining multiple indexes. +- SaaS Arbitrary Filtering Workloads: In SaaS workloads, there is often a need for arbitrary filtering across many columns. Indexing all columns is impractical, especially when the primary key includes a tenant ID and all queries include this ID. TiFlash is an ideal solution here: because data in TiFlash is sorted and clustered by primary key, and with the [late materialization](/tiflash/tiflash-late-materialization.md) feature, it enables efficient table range scans. This allows TiFlash to provide fast performance without the overhead of maintaining multiple indexes. Using TiFlash strategically can enhance query performance and optimize resource usage in TiDB for data-intensive, analytical queries. Here are two use case of TiFlash @@ -882,8 +892,8 @@ Consider a table design with a composite primary key: (tenantId, objectTypeId, o #### Performance Comparison -- TiKV Plan: When executing this query on TiKV storage engine, it takes 2 minutes 38.6 seconds as it requires a table range scan operation, and cannot utilize indexes effectively for dynamic filtering conditions across hundreds of columns. -- TiFlash Plan: The same query on TiFlash MPP engine takes only 3.44 seconds - almost 46 times faster. Since data in TiFlash is sorted by primary key, queries filtered by the primary key's prefix will also use a TableRangeScan instead of a full table scan. +- TiKV Plan: When executing this query on TiKV storage engine, it takes 2 minutes 38.6 seconds. `TableRangeScan` need to send 5121 cop tasks since data is spread over 5121 regions. +- TiFlash Plan: The same query on TiFlash MPP engine takes only 3.44 seconds - almost 46 times faster. Since data in TiFlash is sorted by primary key, queries filtered by the primary key's prefix will also use a `TableRangeScan` instead of a full table scan. Compared to TiKV, TiFlash only uses 2 mpp tasks. ```sql WITH `results` AS ( @@ -946,7 +956,7 @@ TiFlash Plan +--------------------------------+-----------+---------+--------------+--------------------+-----------------------------------------------------+ ``` -#### Intelligent Query Routing +#### Query Routing between TiKV and TiFlash After enabling TiFlash replicas for tables with large amounts of multi-tenant data, the optimizer can make choices to serve the query on TiKV or TiFlash storage engine based on the row count: From c8f7882da034f508200e82fb3488f74f47ede9b2 Mon Sep 17 00:00:00 2001 From: dbsid Date: Sat, 7 Dec 2024 14:11:32 +0800 Subject: [PATCH 38/46] fix format --- sql-tuning-best-practice.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index aecccfff53889..013ad7690027f 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -795,9 +795,10 @@ Plan with new index `idx_composite` | IndexLookUp_32 | 101.00 | 101 | root | | time:5.3ms, loops:2, RU:3.435006, index_task: {t...}| limit embedded(offset:0, count:101) | 128.5 KB | N/A | | ├─Limit_31(Build) | 101.00 | 101 | cop[tikv] | | time:1.35ms, loops:1, cop_task: {num: 1, max: 1....}| offset:0, count:101 | N/A | N/A | | │ └─Selection_30 | 535.77 | 224 | cop[tikv] | | tikv_task:{time:0s, loops:3} | ge(orders.created_at, 2024-04-07 18:07:52), le(orders.created_at, 2024-05-11 18:07:52), not(isnull(orders.label_id)) | N/A | N/A | -| │ └─IndexRangeScan_28 | 503893.42 | 224 | cop[tikv] | table:orders, index:idx_composite(user_id, mode, id, created_at, label_id) | tikv_task:{time:0s, loops:3} | range:[11111 "production" 1000000000,11111 "production" 1500000000), keep order:true, desc | N/A | N/A | +| │ └─IndexRangeScan_28 | 503893.42 | 224 | cop[tikv] | table:orders, index:idx_composite(user_id, mode, id, created_at, label_id) | tikv_task:{time:0s, loops:3} | range:[11111 "production" 1000000000,11111 "production" 1500000000), keep order:true, desc | N/A | N/A | | └─TableRowIDScan_29(Probe) | 101.00 | 101 | cop[tikv] | table:orders | time:2.9ms, loops:2, cop_task: {num: 3, max: 2.7...}| keep order:false | N/A | N/A | +--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ + ``` ## When to Use TiFlash: A Simple Guide From ad2efeaf213e2a0ff8230449b8caf8b1e88b6984 Mon Sep 17 00:00:00 2001 From: dbsid Date: Sat, 7 Dec 2024 15:31:24 +0800 Subject: [PATCH 39/46] unify the terminology TiDB layer --> TiDB Server TiKV layer --> TiKV TiFlash layer --> TiFlash --- sql-tuning-best-practice.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index 013ad7690027f..653a681fe0ec8 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -295,9 +295,9 @@ Also, the optimizer need to evaluate operations that manipulate row sets, such a Furthermore, expression and operator push-down to the physical storage engines is part of the physical optimization phase. The physical plan is distributed to different components based on the underlying storage engines: -- Root Task runs at the TiDB layer -- Cop (Coprocessor) Task runs at the TiKV layer -- MPP (Massively Parallel Processing) Task runs at the TiFlash layer +- Root Task runs at the TiDB Server +- Cop (Coprocessor) Task runs at the TiKV +- MPP (Massively Parallel Processing) Task runs at the TiFlash This distribution of the physical plan allows the different components to collaborate and execute the query efficiently. @@ -315,7 +315,7 @@ Beside access the execution plan information through TiDB Dashboard, TiDB provid - id: Operator name and the step unique identifier - estRows: Estimated number of rows from the particular step -- task: Indicates the layer where the operator is executed. For instance, `root` indicates execution at the TiDB layer, whereas `cop[tikv]` indicates execution at the TiKV layer, and `mpp[tiflash]` indicates execution at the TiFlash layer. +- task: Indicates the layer where the operator is executed. For instance, `root` indicates execution at the TiDB Server, whereas `cop[tikv]` indicates execution at the TiKV, and `mpp[tiflash]` indicates execution at the TiFlash. - access object: The object where the row sources are located - operator info: Extended information about the operator regarding the step @@ -549,7 +549,7 @@ More Detail for understanding execution plans : [TiDB Query Execution Plan Overv ## Index Strategy in TiDB -TiDB is a distributed SQL database that completely decouples the SQL layer (TiDB) from the storage layer (TiKV). Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. Typical KV RPC are `Point_Get`, `Batch_Point_Get`, and Cop tasks. +TiDB is a distributed SQL database that completely decouples the SQL layer (TiDB Server) from the storage layer (TiKV). Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. Typical KV RPC are `Point_Get`, `Batch_Point_Get`, and Cop tasks. In TiDB, leveraging indexes effectively is crucial for performance tuning, as it can significantly reduce the number of KV RPC requests. By minimizing these requests, you can greatly improve query performance and overall system efficiency. Here are some key strategies: @@ -819,17 +819,17 @@ The original query requires joining the order_line and item tables. The plan for In the TiKV plan: - TiDB needs to fetch 3,864,397 rows from the lineitem table and 10 million rows from the part table. -- The hash join operation (HashJoin_21) is performed at the TiDB layer, along with the subsequent projection (Projection_38) and aggregation (HashAgg_9) operations. +- The hash join operation (HashJoin_21) is performed at the TiDB, along with the subsequent projection (Projection_38) and aggregation (HashAgg_9) operations. The plan for executing the same query on the TiFlash MPP (Massively Parallel Processing) takes only 1.41 seconds, which is 15 times faster than the TiKV plan. In the TiFlash MPP plan: - The optimizer recognizes that both the order_line and item tables have TiFlash replicas available. -- As a result, the optimizer is able to push down the entire query execution, including the table scans, hash join, column projection, and aggregation, to the TiFlash MPP layer. -- Performing the entire query execution on the TiFlash MPP layer, which is optimized for analytical workloads, leads to the significant performance improvement compared to the TiKV plan. +- As a result, the optimizer is able to push down the entire query execution to the TiFlash, including the table scans, hash join, column projection, and aggregation. +- Performing the entire query execution on the TiFlash, which is optimized for analytical workloads, leads to the significant performance improvement compared to the TiKV plan. -The key difference between the two plans is that the TiFlash MPP plan can leverage the distributed processing capabilities of the TiFlash MPP layer to execute the entire query more efficiently, while the TiKV plan requires more coordination between the TiDB and TiKV components. +The key difference between the two plans is that the TiFlash MPP plan can leverage the distributed processing capabilities of the TiFlash to execute the entire query more efficiently, while the TiKV plan requires more coordination between the TiDB and TiKV components. ```sql select 100.00 * sum(case when i_data like 'PR%' then ol_amount else 0 end) / (1+sum(ol_amount)) as promo_revenue @@ -964,5 +964,5 @@ After enabling TiFlash replicas for tables with large amounts of multi-tenant da - Small Tenants: TiKV is preferred for tenants with small data size, as it provides high concurrency for small queries with table range scan. - Large Tenants: For tenants with large datasets (like 10M rows in this case), TiFlash is more efficient as it: - Handles dynamic filtering conditions without requiring specific indexes - - Pushes down count, sort and limit operations to TiFlash MPP layer + - Pushes down count, sort and limit operations to TiFlash - Leverages columnar storage to scan only the required columns \ No newline at end of file From 03b6659a11af25dc821f9f33f41ed18ed0d1b7a7 Mon Sep 17 00:00:00 2001 From: dbsid Date: Sat, 7 Dec 2024 16:27:23 +0800 Subject: [PATCH 40/46] simple the terminology --- sql-tuning-best-practice.md | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index 653a681fe0ec8..3a99d5b76e4db 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -296,8 +296,8 @@ Also, the optimizer need to evaluate operations that manipulate row sets, such a Furthermore, expression and operator push-down to the physical storage engines is part of the physical optimization phase. The physical plan is distributed to different components based on the underlying storage engines: - Root Task runs at the TiDB Server -- Cop (Coprocessor) Task runs at the TiKV -- MPP (Massively Parallel Processing) Task runs at the TiFlash +- Cop (Coprocessor) Task runs at TiKV +- MPP Task runs at TiFlash This distribution of the physical plan allows the different components to collaborate and execute the query efficiently. @@ -315,7 +315,7 @@ Beside access the execution plan information through TiDB Dashboard, TiDB provid - id: Operator name and the step unique identifier - estRows: Estimated number of rows from the particular step -- task: Indicates the layer where the operator is executed. For instance, `root` indicates execution at the TiDB Server, whereas `cop[tikv]` indicates execution at the TiKV, and `mpp[tiflash]` indicates execution at the TiFlash. +- task: Indicates the layer where the operator is executed. For instance, `root` indicates execution at the TiDB Server, whereas `cop[tikv]` indicates execution at TiKV, and `mpp[tiflash]` indicates execution at TiFlash. - access object: The object where the row sources are located - operator info: Extended information about the operator regarding the step @@ -805,7 +805,7 @@ Plan with new index `idx_composite` This section explains the best scenarios for using TiFlash in TiDB. TiFlash is optimized for analytical queries that involve complex calculations, aggregations, and large dataset scans, thanks to its columnar storage format. Here’s when to consider TiFlash: -- Large-Scale Data Analysis: For queries involving extensive data scans, such as OLAP workloads, TiFlash can deliver faster performance compared to TiKV due to its columnar storage and MPP capability. +- Large-Scale Data Analysis: For queries involving extensive data scans, such as OLAP workloads, TiFlash can deliver faster performance compared to TiKV due to its columnar storage and MPP mode. - Complex Scans, Aggregations and Joins: TiFlash is ideal for queries with heavy aggregations and joins, as it can process these operations more efficiently by only reading necessary columns. - Mixed Workloads: In hybrid environments where both transactional (OLTP) and analytical (OLAP) workloads run simultaneously, TiFlash can handle analytical queries without impacting TiKV’s performance for transactional queries. - SaaS Arbitrary Filtering Workloads: In SaaS workloads, there is often a need for arbitrary filtering across many columns. Indexing all columns is impractical, especially when the primary key includes a tenant ID and all queries include this ID. TiFlash is an ideal solution here: because data in TiFlash is sorted and clustered by primary key, and with the [late materialization](/tiflash/tiflash-late-materialization.md) feature, it enables efficient table range scans. This allows TiFlash to provide fast performance without the overhead of maintaining multiple indexes. @@ -819,17 +819,16 @@ The original query requires joining the order_line and item tables. The plan for In the TiKV plan: - TiDB needs to fetch 3,864,397 rows from the lineitem table and 10 million rows from the part table. -- The hash join operation (HashJoin_21) is performed at the TiDB, along with the subsequent projection (Projection_38) and aggregation (HashAgg_9) operations. +- The hash join operation (`HashJoin_21`) is performed at the TiDB, along with the subsequent projection (`Projection_38`) and aggregation (`HashAgg_9`) operations. -The plan for executing the same query on the TiFlash MPP (Massively Parallel Processing) takes only 1.41 seconds, which is 15 times faster than the TiKV plan. +The plan for executing the same query using TiFlash MPP mode takes only 1.41 seconds, which is 15 times faster than the TiKV plan. -In the TiFlash MPP plan: +In the TiFlash plan using MPP mode: -- The optimizer recognizes that both the order_line and item tables have TiFlash replicas available. -- As a result, the optimizer is able to push down the entire query execution to the TiFlash, including the table scans, hash join, column projection, and aggregation. -- Performing the entire query execution on the TiFlash, which is optimized for analytical workloads, leads to the significant performance improvement compared to the TiKV plan. - -The key difference between the two plans is that the TiFlash MPP plan can leverage the distributed processing capabilities of the TiFlash to execute the entire query more efficiently, while the TiKV plan requires more coordination between the TiDB and TiKV components. +- The optimizer recognizes that both the order_line and item tables have TiFlash replicas available. TiDB automatically selects the MPP mode based on the optimizer's cost estimation. +- Under the MPP mode, the entire query is executed on TiFlash columnar storage engine, including the table scans, hash join, column projection, aggregation, leads to the significant performance improvement compared to the TiKV plan. + +SQL query ```sql select 100.00 * sum(case when i_data like 'PR%' then ol_amount else 0 end) / (1+sum(ol_amount)) as promo_revenue @@ -855,7 +854,7 @@ TiKV Plan +-------------------------------+--------------+-----------+-----------+----------------+----------------------------------------------+ ``` -TiFlash MPP Plan +TiFlash Plan using MPP mode ``` +--------------------------------------------+-------------+----------+--------------+----------------+--------------------------------------+ From 153ff39506e413b1e7d9d28474998584fcbec90f Mon Sep 17 00:00:00 2001 From: dbsid Date: Sat, 7 Dec 2024 17:31:19 +0800 Subject: [PATCH 41/46] improve the formats --- sql-tuning-best-practice.md | 102 +++++++++++++++++------------------- 1 file changed, 48 insertions(+), 54 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index 3a99d5b76e4db..1b2988cf76c00 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -130,11 +130,11 @@ This guide focuses on providing actionable advice for beginners looking to optim - How TiDB Builds A Execution Plan - Understand Execution Plan - Index Strategy in TiDB + - Composite Index Strategy Guidelines + - The Cost of Indexing - SQL Tuning with a Covered Index - SQL Tuning with a Composite Index Involing Sorting - SQL Tuning with a Composite Index for Efficient Filtering and Sorting - - Composite Index Strategy Guidelines - - The Cost of Indexing - When to Use TiFlash: A Simple Guide ## Query Processing Workflow @@ -281,7 +281,7 @@ explain SELECT id, name FROM emp WHERE id = 901; ### Logical Transformation -The purpose of logical Transformation is to optimize the execution of statements based on the characteristics of SELECT list, WHERE predicates, and other predicates in SQL queries. It generates a logical execution plan to annotate and rewrite the query. This logical plan is then passed to the Cost-Based Optimization. The optimization rules are such as column pruning, partition pruning, join reorder etc. Since this step is rule-based and automated by the query optimizer, in most casesit usually does not require manual adjustments. +The purpose of logical Transformation is to optimize the execution of statements based on the characteristics of SELECT list, WHERE predicates, and other predicates in SQL queries. It generates a logical execution plan to annotate and rewrite the query. This logical plan is then passed to the Cost-Based Optimization. The optimization rules are such as column pruning, partition pruning, join reorder etc. Since this step is rule-based and automated by the query optimizer, in most cases it usually does not require manual adjustments. More Detail for Logical Transformation: [SQL Logical Optimization](/sql-logical-optimization.md). @@ -346,6 +346,7 @@ Description - disk: Disk space used by the operator. Note: Some attributes and explain table columns are omitted for improved formatting +Here is the query statement: ```sql EXPLAIN ANALYZE @@ -389,8 +390,6 @@ To understand why SQL queries run slowly, it's very important to know how to rea The "first child first" rule means that before an operator can produce its output rows, it must first get the rows from all its child operators. For example, a join operator needs rows from both its child operators before it can perform the join operation. The "recursive descent" rule means we analyze the plan from top to bottom, but the actual data flows from bottom to top, as each operator depends on its children's output. -The traversal of the execution plan follows a top-to-bottom, first-child-first approach. This traversal pattern corresponds to a postorder (Left, Right, Root) traversal of the plan tree. - There are two important details to add to this: 1. Parent-Child Interaction: Although a parent operator calls its child operators in sequence, it may cycle through them multiple times. For example, in an index lookup or nested loop join, the parent fetches a batch of rows from the first child, then (zero or more) rows from the second child, repeating this process until it consumes the entire result set from the first child. @@ -418,6 +417,8 @@ EXPLAIN SELECT COUNT(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00 Let's apply the "first child first – recursive descent" rule to the second plan. In the below example begin from the top to bottom, by looking at the `IndexRangeScan_47` (the first leaf of the plan tree). For the table `stars`, the optimizer only needs to select the column `name` and `id`, the two columns can be met by the index `name(name)`. So for the table `star`, the root reader is `IndexReader_48`, rather than a `TableReader`. The join method between `stars` and `planets` is a hash join, which is marked as `HashJoin_44`. The data access method on `planets` is a `TableFullScan_45`. After the join, the `TopN_26` and `TOPN_19` are to implement the `ORDER BY` and `LIMIT` clauses correspondingly. The final operator `Projection_16` is to implement the column projection for `t5.name`. +Here is the query statement: + ```sql EXPLAIN SELECT t5.name @@ -457,16 +458,16 @@ The traversal of the execution plan follows a top-to-bottom, first-child-first a To read this plan: -1. Start at the top with Projection_16 -2. Move to its child, TopN_19 -3. Continue to TopN_26 -4. Proceed to HashJoin_44 -5. For HashJoin_44, first process its left (Build) child: - - IndexReader_48 - - IndexRangeScan_47 +1. Start at the top with `Projection_16` +2. Move to its child, `TopN_19` +3. Continue to `TopN_26` +4. Proceed to `HashJoin_44` +5. For `HashJoin_44`, first process its left (Build) child: + - `IndexReader_48` + - `IndexRangeScan_47` 6. Then process its right (Probe) child: - - TableReader_46 - - TableFullScan_45 + - `TableReader_46` + - `TableFullScan_45` This traversal ensures that each operator's inputs are processed before the operator itself, allowing for efficient execution of the query plan. @@ -479,18 +480,15 @@ To identify the bottleneck in a poorly performing query: 1. Scan the `execution info` section from top to bottom, looking for operators that consume a significant amount of time. 2. For the first child operator with significant time consumption, analyze the following: - `actRows`: Compare with `estRows` to check for estimation accuracy. - - Detailed measurements in `execution info`: Look for high values in time, loops, or other metrics. + - Detailed measurements in `execution info`: Look for high values in time, or other metrics. - `memory` and `disk` usage: High values may indicate suboptimal plan or resource constraints. -3. Correlate these factors to determine the root cause of the performance issue. For example, if you see a `TableFullScan` operation with a high `actRows` count and significant time in `execution info`, it might suggest the need for an index. Alternatively, if a `HashJoin` operation shows high memory usage and time, you might need to optimize the join or consider alternative join methods. +3. Correlate these factors to determine the root cause of the performance issue. For example, if you see a `TableFullScan` operation with a high `actRows` count and significant time in `execution info`, it might suggest the need for an index. Alternatively, if a `HashJoin` operation shows high memory usage and time, you might need to optimize the join order or consider alternative join methods. In the execution plan below, the query ran for 5 minutes and 51 seconds before being canceled. Let's analyze the key issues: -1. Severe underestimation: The first child operator `IndexReader_76` reads data from the index `index_orders_on_adjustment_id(adjustment_id)`. The actual number of rows (`actRows`) is 256,811,189, which is drastically higher than the estimated 1 row (`estRows`). - +1. Severe underestimation: The first leaf node `IndexReader_76` reads data from the index `index_orders_on_adjustment_id(adjustment_id)`. The actual number of rows (`actRows`) is 256,811,189, which is drastically higher than the estimated 1 row (`estRows`). 2. Memory overflow: Due to this underestimation, the hash join operator `HashJoin_69` attempts to build a hash table with far more data than anticipated. This results in excessive memory usage (22.6GB) and disk usage (7.65GB). - 3. Query termination: The `actRows` is 0 for `HashJoin_69` and the operators above, indicating either there is no rows matched or the query is terminated due to resource constraints. In this case, the hash join consumed too much memory, causing the query to be terminated by memory control mechanisms. - 4. Incorrect join order: The root cause of this inefficient plan is the severe underestimation of `estRows` for `IndexRangeScan_75`, which led to an incorrect join order decision by the optimizer. To address these issues, need to ensure that table statistics are up-to-date, especially for the `orders` table and the `index_orders_on_adjustment_id` index. @@ -521,7 +519,7 @@ Here is the expected execution plan after fixing the incorrect estimation on the 2. Efficient join order: The query now starts with a `TableReader` on the `labels` table, followed by an `IndexJoin` with the `rates` table, and finally another `IndexJoin` with the `orders` table. This join order is more efficient given the actual data distribution. 3. No memory overflow: Unlike the previous plan, there are no signs of excessive memory or disk usage, indicating that the query executes within expected resource limits. -This optimized plan demonstrates the importance of accurate statistics and proper join order in query performance. The dramatic reduction in execution time (from 351 seconds to 1.96 seconds) highlights the potential impact of addressing estimation errors and choosing appropriate execution strategies. +This optimized plan demonstrates the importance of accurate statistics and proper join order in query performance. The dramatic reduction in execution time (from 351 seconds to 1.96 seconds) highlights the impact of addressing estimation errors and choosing appropriate execution strategies. ``` +---------------------------------------+----------+---------+-----------+----------------------------------------------------------------------------------------+---------------...+----------+------+ @@ -549,7 +547,7 @@ More Detail for understanding execution plans : [TiDB Query Execution Plan Overv ## Index Strategy in TiDB -TiDB is a distributed SQL database that completely decouples the SQL layer (TiDB Server) from the storage layer (TiKV). Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. Typical KV RPC are `Point_Get`, `Batch_Point_Get`, and Cop tasks. +TiDB is a distributed SQL database that completely decouples the SQL layer (TiDB Server) from the storage layer (TiKV). Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. Typical KV RPC are `Point_Get`, `Batch_Point_Get`, and Coprocessor. In TiDB, leveraging indexes effectively is crucial for performance tuning, as it can significantly reduce the number of KV RPC requests. By minimizing these requests, you can greatly improve query performance and overall system efficiency. Here are some key strategies: @@ -557,7 +555,7 @@ In TiDB, leveraging indexes effectively is crucial for performance tuning, as it - Avoiding sorting - Skipping row lookups when possible by using covering index or not request non-needed columns -This section explains the general index strategy and the cost of index. It showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covering indexes for both filtering and sorting operations. +This section explains the general index strategy and the cost of indexing. Then it showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covering indexes for both filtering and sorting operations. ### Composite Index Strategy Guidelines @@ -567,7 +565,7 @@ Here is the recommended order for the columns in the index: 1. Equal predicates for index prefix (columns accessed directly): - Equal conditions - - IS NULL conditions + - `IS NULL` conditions 2. Columns after index prefix for sorting: - Leverage index to preprocess sorting @@ -575,11 +573,10 @@ Here is the recommended order for the columns in the index: - Maintain sorted order 3. Additional filtering columns, to reduce row lookups: - - Non-equal predicates (`!=`, `<>`, `IS NOT NULL`, etc) - Time range conditions on datetime columns - - Helps reduce row lookups + - other non-equal predicates (`!=`, `<>`, `IS NOT NULL`, etc) -4. Columns in index postfix for select list or aggregattion, to avoid row lookups +4. Include columns in the SELECT list or aggregation to fully utilize a covering index. ### The Cost of Indexing @@ -611,6 +608,8 @@ Best Practice: A covering index is designed to include all columns referenced in the filter and select clauses. The query below requires an index lookup of 2597411 rows, taking 46.4 seconds to execute. TiDB needs to dispatch 67 cop tasks for the index range scan on logs_idx, identified as `IndexRangeScan_11`, and 301 cop tasks for table access via `TableRowIDScan_12`. By utilizing a covering index, the index lookup can be avoided, leading to improved performance. +Here is the query statement: + ```sql SELECT SUM(`logs`.`amount`) @@ -645,7 +644,7 @@ WHERE After creating the covered index below, which includes the additional columns `source_type`, `target_type`, and `amount`, the query execution time improved to 90ms, and TiDB only needed to send a single cop task to TiKV for data scanning. -Be noted after creating the index, the `ANALYZE TABLE` command is used to gather the statistics for the index. So that the optimizer can make the best use of the index. Since In TiDB, create index will not gather the statistics for the index. +Be noted after creating the index, the `ANALYZE TABLE` command is used to gather the statistics for the index. So that the optimizer can leverage the new index immediately. Since In TiDB, index creation will not gather the statistics for the index automatically. ```sql CREATE INDEX logs_covered ON logs(snapshot_id, user_id, status, source_type, target_type, amount); @@ -670,6 +669,8 @@ When optimizing SQL queries, especially those that include an `ORDER BY` clause, For instance, consider the following query that retrieves data from `test` based on specific conditions. The execution plan shows a duration of 170ms. TiDB employs the `test_index` to perform an `IndexRangeScan_20` with the filter `snapshot_id = 459840`. Subsequently, it retrieves all columns from the table, resulting in 5715 rows being streamed back to TiDB after the `IndexLookUp_23`, which then sorts the dataset and returns 1000 rows. Note that the `id` column is the primary key, which means it is implicitly included in the `test_idx` index. However, for `IndexRangeScan_20`, the order is not guaranteed because, after the index prefix column `snapshot_id`, there are two additional columns: `user_id` and `status`. Therefore, the ordering of `id` cannot be assured. +Here is the query statement: + ```sql EXPLAIN ANALYZE SELECT test.* @@ -699,10 +700,12 @@ Original Plan +------------------------------+---------+---------+-----------+----------------------------------------------------------+-----------------------------------------------+--------------------------------------------+ ``` -To optimize the query, we can create a new index on `(snapshot_id, id)` to ensure that for each snapshot_id value, the id is sorted in the index. By utilizing this new index, the query execution time is reduced to 96ms. Note that the keep order is true for `IndexRangeScan_33`, and the `TopN` is replaced with `Limit`. For the `IndexLookUp_35`, only 1000 rows are returned to TiDB, eliminating the need for additional sorting operations. +To optimize the query, we can create a new index on `(snapshot_id)` to ensure that for each snapshot_id value, the id is sorted in the index. By utilizing this new index, the query execution time is reduced to 96ms. Note that the `keep order` is true for `IndexRangeScan_33`, and the `TopN` is replaced with `Limit`. For the `IndexLookUp_35`, only 1000 rows are returned to TiDB, eliminating the need for additional sorting operations. + +Here is the query statement: ```sql -CREATE INDEX test_new ON test(snapshot_id, id); +CREATE INDEX test_new ON test(snapshot_id); ANALYZE TABLE test INDEX test_new; ``` @@ -714,7 +717,7 @@ New Plan +----------------------------------+---------+---------+-----------+----------------------------------------------+----------------------------------------------+----------------------------------------------------+ | Limit_14 | 17.59 | 1000 | root | | time:96.1ms, loops:2, RU:92.300155 | offset:0, count:1000 | | └─IndexLookUp_35 | 17.59 | 1000 | root | | time:96.1ms, loops:1, index_task: ...| | -| ├─IndexRangeScan_33(Build) | 17.59 | 5715 | cop[tikv] | table:test, index:test_new(snapshot_id, id) | time:7.25ms, loops:8, cop_task: {num: 3, ...| range:(459840 998464,459840 +inf], keep order:true | +| ├─IndexRangeScan_33(Build) | 17.59 | 5715 | cop[tikv] | table:test, index:test_new(snapshot_id) | time:7.25ms, loops:8, cop_task: {num: 3, ...| range:(459840 998464,459840 +inf], keep order:true | | └─TableRowIDScan_34(Probe) | 17.59 | 5715 | cop[tikv] | table:test | time:232.9ms, loops:9, cop_task: {num: 3, ...| keep order:false | +----------------------------------+---------+---------+-----------+----------------------------------------------+----------------------------------------------+----------------------------------------------------+ ``` @@ -725,10 +728,10 @@ The original query below took 11 minutes and 9 seconds to complete, which is an 1. Inefficient index usage: The optimizer chose the index on `created_at`, which resulted in scanning 25,147,450 rows. 2. Large intermediate result set: After applying the date range filter, 12,082,311 rows still needed to be processed. -3. Late filtering: The most selective predicates (mode, user_id, and label_id) were applied after accessing the table, resulting in 16,604 rows. +3. Late filtering: The most selective predicates `(mode, user_id, and label_id)` were applied after accessing the table, resulting in 16,604 rows. 4. Sorting overhead: The final sort operation on 16,604 rows added additional processing time. -Here is the query pattern +Here is the query statement: ```sql SELECT `orders`.* @@ -770,12 +773,10 @@ Original plan +--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------+----------+------+ ``` -Performance Improvement with the New Index: - After creating the new index `idx_composite` on `orders(user_id, mode, id, created_at, label_id)`, the query performance improved dramatically. The execution time reduced from 11 minutes and 9 seconds to just 5.3 milliseconds, which is a staggering improvement of over 126,000 times faster. This massive improvement can be explained by: 1. Efficient index usage: The new index allows for an index range scan on user_id, mode, and id, which are the most selective predicates. This drastically reduces the number of rows scanned from millions to just 224. -2. Index-only sort: The 'keep order:true' in the plan indicates that the index structure is used for sorting, avoiding a separate sort operation. +2. Index-only sort: The `keep order:true` in the plan indicates that the index structure is used for sorting, avoiding a separate sort operation. 3. Early filtering: The most selective predicates are applied first, quickly reducing the result set to 224 rows before further filtering. 4. Limit push-down: The LIMIT clause is pushed down to the index scan, allowing early termination of the scan once 101 rows are found. @@ -814,21 +815,12 @@ Using TiFlash strategically can enhance query performance and optimize resource ### Analytical Query -The original query requires joining the order_line and item tables. The plan for executing this query on the TiKV storage engine takes 21.1 seconds. - -In the TiKV plan: +Take the TPC-H Query 14 as an example. The original query requires joining the order_line and item tables. The plan for executing this query on the TiKV storage engine takes 21.1 seconds. The plan for executing the same query using TiFlash MPP mode takes only 1.41 seconds, which is 15 times faster than the TiKV plan. -- TiDB needs to fetch 3,864,397 rows from the lineitem table and 10 million rows from the part table. -- The hash join operation (`HashJoin_21`) is performed at the TiDB, along with the subsequent projection (`Projection_38`) and aggregation (`HashAgg_9`) operations. - -The plan for executing the same query using TiFlash MPP mode takes only 1.41 seconds, which is 15 times faster than the TiKV plan. - -In the TiFlash plan using MPP mode: - -- The optimizer recognizes that both the order_line and item tables have TiFlash replicas available. TiDB automatically selects the MPP mode based on the optimizer's cost estimation. -- Under the MPP mode, the entire query is executed on TiFlash columnar storage engine, including the table scans, hash join, column projection, aggregation, leads to the significant performance improvement compared to the TiKV plan. +- TiKV plan: TiDB needs to fetch 3,864,397 rows from the lineitem table and 10 million rows from the part table. The hash join operation (`HashJoin_21`) is performed at the TiDB, along with the subsequent projection (`Projection_38`) and aggregation (`HashAgg_9`) operations. +- TiFlash plan: The optimizer recognizes that both the order_line and item tables have TiFlash replicas available. TiDB automatically selects the MPP mode based on the optimizer's cost estimation. Under the MPP mode, the entire query is executed on TiFlash columnar storage engine, including the table scans, hash join, column projection and aggregation, leads to the significant performance improvement compared to the TiKV plan. -SQL query +Here is the query statement: ```sql select 100.00 * sum(case when i_data like 'PR%' then ol_amount else 0 end) / (1+sum(ol_amount)) as promo_revenue @@ -836,7 +828,7 @@ from order_line, item where ol_i_id = i_id and ol_delivery_d >= '2007-01-02 00:00:00.000000' and ol_delivery_d < '2030-01-02 00:00:00.000000'; ``` -TiKV Plan +TiKV plan ``` +-------------------------------+--------------+-----------+-----------+----------------+----------------------------------------------+ @@ -854,7 +846,7 @@ TiKV Plan +-------------------------------+--------------+-----------+-----------+----------------+----------------------------------------------+ ``` -TiFlash Plan using MPP mode +TiFlash plan ``` +--------------------------------------------+-------------+----------+--------------+----------------+--------------------------------------+ @@ -885,7 +877,7 @@ In SaaS applications, it's common to structure tables with composite primary key #### Case Study: Multi-tenant Data Access -Consider a table design with a composite primary key: (tenantId, objectTypeId, objectId). A common query pattern is to: +Consider a table design with a composite primary key: `(tenantId, objectTypeId, objectId)`. A common query pattern is to: - Fetch the first N records for a specific tenant and object type, and applying random filters across hundreds or thousands of other columns, Making it impractical to create indexes for every possible filter combination. There is a potential sort operator after the filter as well. - Get the total count of records matching these criterias @@ -895,6 +887,8 @@ Consider a table design with a composite primary key: (tenantId, objectTypeId, o - TiKV Plan: When executing this query on TiKV storage engine, it takes 2 minutes 38.6 seconds. `TableRangeScan` need to send 5121 cop tasks since data is spread over 5121 regions. - TiFlash Plan: The same query on TiFlash MPP engine takes only 3.44 seconds - almost 46 times faster. Since data in TiFlash is sorted by primary key, queries filtered by the primary key's prefix will also use a `TableRangeScan` instead of a full table scan. Compared to TiKV, TiFlash only uses 2 mpp tasks. +Here is the query statement: + ```sql WITH `results` AS ( SELECT field1, field2, field3, field4 @@ -916,7 +910,7 @@ FROM ) `result_and_count`; ``` -TiKV Plan +TiKV plan ``` +--------------------------------+-----------+---------+-----------+-----------------------+-----------------------------------------------------+ @@ -935,7 +929,7 @@ TiKV Plan +--------------------------------+-----------+---------+-----------+-----------------------+-----------------------------------------------------+ ``` -TiFlash Plan +TiFlash plan ``` +--------------------------------+-----------+---------+--------------+--------------------+-----------------------------------------------------+ From 7ffcae6d66e52df033f81ec6f3eac81663a48b59 Mon Sep 17 00:00:00 2001 From: dbsid Date: Sat, 7 Dec 2024 17:42:44 +0800 Subject: [PATCH 42/46] fix --- sql-tuning-best-practice.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index 1b2988cf76c00..cf79211506617 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -144,8 +144,8 @@ The client sends a SQL statement to the protocol layer of TiDB server. The proto To the right of the protocol layer is the optimizer of TiDB server, which is responsible for processing SQL statements. The process is as follows: 1. SQL statement arrives at the SQL optimizer through the protocol layer and is first parsed into an Abstract Syntax Tree (AST). -2. Pre-Process is primarily for Point Get, a simple, one table lookup through a Primary or Unique Key like `SELECT * FROM t WHERE pk_col = X` or `SELECT * FROM t WHERE uk_col IN (X,Y,Z)`. If it is a Point Get, the following-up optimization processes can be skipped, the next step jumps to the SQL Executor. -3. After confirming that it is not a Point Get, the AST goes to the Logical Transformation, which rewrites the SQL logically based on certain rules. +2. Pre-Process is primarily for [Point Get](/explain-indexes#point_get-and-batch_point_get), a simple, one table lookup through a Primary or Unique Key like `SELECT * FROM t WHERE pk_col = 1` or `SELECT * FROM t WHERE uk_col IN (1,2,3)`. If it is a `Point Get`, the following-up optimization processes can be skipped, the next step jumps to the SQL Executor. +3. After confirming that it is not a `Point Get`, the AST goes to the Logical Transformation, which rewrites the SQL logically based on certain rules. 4. The AST that has gone through Logical Transformation will undergo Cost-Based Optimization. 5. During Cost-Based Optimization, the optimizer considers statistics to determine how to select specific operators and finally generates a physical execution plan. 6. The generated physical execution plan is sent to the SQL Executor of the TiDB node for execution. @@ -261,9 +261,9 @@ An SQL statement undergoes optimization primarily in the optimizer through three ### Pre-Processing -The main actions in the pre-processing stage it to determine if the SQL statement can be executed by using [`Point_Get`](https://docs.pingcap.com/tidb/stable/explain-indexes#point_get-and-batch_point_get) or [`Batch_Point_Get`](https://docs.pingcap.com/tidb/stable/explain-indexes#point_get-and-batch_point_get). +The main actions in the pre-processing stage it to determine if the SQL statement can be executed by using [`Point_Get`](/explain-indexes#point_get-and-batch_point_get) or [`Batch_Point_Get`](/explain-indexes#point_get-and-batch_point_get). -`Point_Get` or `Batch_Point_Get` means using a primary or unique key, to directly read from TiKV, by exact key lookup. For example, when id column is the primary key of a [clustered index](/clustered-indexes.md) table, `Point_Get` is used to get the particular row. If a plan is identified as `Point_Get`, optimizer will skip the logical transformation and cost-based optimization, since the exact key read will be the best way to access the row. `Batch_Point_Get` will read a batch of exact key lookups, like `SELECT * FROM t WHERE id IN (4,62,81)` +`Point_Get` or `Batch_Point_Get` means using a primary or unique key, to directly read from TiKV, by exact key lookup. If a plan is identified as `Point_Get`, optimizer will skip the logical transformation and cost-based optimization, since the exact key read will be the best way to access the row. Example: From 2dadad620114733368999e425c8c67be874f0bb7 Mon Sep 17 00:00:00 2001 From: dbsid Date: Sat, 7 Dec 2024 19:04:30 +0800 Subject: [PATCH 43/46] fix format --- sql-tuning-best-practice.md | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index cf79211506617..77881e5924b9a 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -261,11 +261,9 @@ An SQL statement undergoes optimization primarily in the optimizer through three ### Pre-Processing -The main actions in the pre-processing stage it to determine if the SQL statement can be executed by using [`Point_Get`](/explain-indexes#point_get-and-batch_point_get) or [`Batch_Point_Get`](/explain-indexes#point_get-and-batch_point_get). +The main actions in the pre-processing stage it to determine if the SQL statement can be executed by using [`Point_Get`](/explain-indexes#point_get-and-batch_point_get) or [`Batch_Point_Get`](/explain-indexes#point_get-and-batch_point_get). `Point_Get` or `Batch_Point_Get` means using a primary or unique key, to directly read from TiKV, by exact key lookup. If a plan is identified as `Point_Get` or `Batch_Point_Get`, optimizer will skip the logical transformation and cost-based optimization, since the exact key read will be the best way to access the row. -`Point_Get` or `Batch_Point_Get` means using a primary or unique key, to directly read from TiKV, by exact key lookup. If a plan is identified as `Point_Get`, optimizer will skip the logical transformation and cost-based optimization, since the exact key read will be the best way to access the row. - -Example: +Here is the query statement: ```sql explain SELECT id, name FROM emp WHERE id = 901; @@ -346,6 +344,7 @@ Description - disk: Disk space used by the operator. Note: Some attributes and explain table columns are omitted for improved formatting + Here is the query statement: ```sql @@ -519,7 +518,7 @@ Here is the expected execution plan after fixing the incorrect estimation on the 2. Efficient join order: The query now starts with a `TableReader` on the `labels` table, followed by an `IndexJoin` with the `rates` table, and finally another `IndexJoin` with the `orders` table. This join order is more efficient given the actual data distribution. 3. No memory overflow: Unlike the previous plan, there are no signs of excessive memory or disk usage, indicating that the query executes within expected resource limits. -This optimized plan demonstrates the importance of accurate statistics and proper join order in query performance. The dramatic reduction in execution time (from 351 seconds to 1.96 seconds) highlights the impact of addressing estimation errors and choosing appropriate execution strategies. +This optimized plan demonstrates the importance of accurate statistics and proper join order in query performance. The dramatic reduction in execution time (from 351 seconds to 1.96 seconds) highlights the impact of addressing estimation errors. ``` +---------------------------------------+----------+---------+-----------+----------------------------------------------------------------------------------------+---------------...+----------+------+ @@ -555,7 +554,7 @@ In TiDB, leveraging indexes effectively is crucial for performance tuning, as it - Avoiding sorting - Skipping row lookups when possible by using covering index or not request non-needed columns -This section explains the general index strategy and the cost of indexing. Then it showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covering indexes for both filtering and sorting operations. +This section explains the general index strategy and the cost of indexing. Then it showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covering indexes for sql tuning. ### Composite Index Strategy Guidelines @@ -629,6 +628,8 @@ WHERE ); ``` +Original plan: + ``` +-------------------------------+------------+---------+-----------+--------------------------------------------------------------------------+------------------------------------------------------------+ | id | estRows | actRows | task | access object | execution info | @@ -651,6 +652,8 @@ CREATE INDEX logs_covered ON logs(snapshot_id, user_id, status, source_type, tar ANALYZE TABLE logs INDEX Logs_covered; ``` +New plan: + ``` +-------------------------------+------------+---------+-----------+---------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------+ | id | estRows | actRows | task | access object | execution info | @@ -685,7 +688,7 @@ LIMIT 1000 ``` -Original Plan +Original plan: ``` +------------------------------+---------+---------+-----------+----------------------------------------------------------+-----------------------------------------------+--------------------------------------------+ @@ -709,7 +712,7 @@ CREATE INDEX test_new ON test(snapshot_id); ANALYZE TABLE test INDEX test_new; ``` -New Plan +New plan: ``` +----------------------------------+---------+---------+-----------+----------------------------------------------+----------------------------------------------+----------------------------------------------------+ @@ -758,7 +761,7 @@ KEY `index_orders_on_label_id` (`label_id`), KEY `index_orders_on_created_at` (`created_at`) ``` -Original plan +Original plan: ``` +--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------+----------+------+ @@ -787,7 +790,7 @@ CREATE INDEX idx_composite ON Orders(user_id, mode, id, created_at, label_id); ANALYZE TABLE orders index idx_composite; ``` -Plan with new index `idx_composite` +New plan: ``` +--------------------------------+-----------+---------+-----------+--------------------------------------------------------------------------------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+----------+------+ From 8796972d97de5cd3d7c948d1fb5307002961869d Mon Sep 17 00:00:00 2001 From: Yui-Song <84501897+Yui-Song@users.noreply.github.com> Date: Thu, 12 Dec 2024 16:25:51 +0800 Subject: [PATCH 44/46] Update sql-tuning-best-practice.md --- sql-tuning-best-practice.md | 74 +++++++++++++++++++------------------ 1 file changed, 38 insertions(+), 36 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index 77881e5924b9a..e5e65b868466b 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -3,7 +3,7 @@ title: A Practical Guide For SQL Tuning summary: Learn how to do SQL tuning in TiDB --- -# Document Purpose and Objectives +# Document purpose and objectives This guide is designed for TiDB SQL tuning beginners, with emphasis on: @@ -13,7 +13,7 @@ This guide is designed for TiDB SQL tuning beginners, with emphasis on: - Gentle Learning Curve: Focusing on practicality over theoretical complexity - Scenario-Based: Incorporating real business cases to demonstrate optimization effects -# Introduction to SQL Tuning +# Introduction to SQL tuning SQL tuning is a critical aspect of optimizing database system performance. It involves a systematic approach to improve the efficiency of SQL queries. The process typically consists of three key steps: @@ -42,7 +42,7 @@ It's important to note that SQL tuning is an ongoing process. As your data volum By consistently applying these practices, you can ensure that your database maintains optimal performance over time. -# Goals for Tuning +# Goals for tuning The primary objectives of SQL tuning are to: @@ -51,7 +51,7 @@ The primary objectives of SQL tuning are to: These goals can be achieved through various strategies: -## Optimize Query Execution +## Optimize query execution SQL tuning often involves finding more efficient ways to process the same workload without changing the query's functionality. This can be done by: @@ -69,17 +69,17 @@ Examples: - Creating an index for frequently queried columns can significantly reduce resource usage, especially for queries that access a small percentage of table data. - Utilizing index-only scans for queries that return a limited number of sorted results can avoid full table scans and sorting operations. -## Balance Workload Distribution +## Balance workload distribution In a distributed architecture like TiDB, it's essential to maintain a balanced workload across multiple TiKV nodes. This is crucial for achieving optimal performance. For guidance on identifying and resolving read and write hotspots, please refer to the [troubleshoot hot spot issues page](/troubleshoot-hot-spot-issues.md#optimization-of-small-table-hotspots). By implementing these strategies, you can ensure that your TiDB cluster efficiently utilizes all available resources and avoids bottlenecks caused by uneven workload distribution or serialization on individual TiKV nodes. -# Identifying High-Load SQL +# Identifying high-load SQL The most efficient way to identify Resource-Intensive SQL is using TiDB Dashboard. There are other tools like views and logs available as well. -## Monitoring SQL Statements by Using TiDB Dashboard +## Monitoring SQL statements by using TiDB Dashboard -### SQL Statements Panel +### SQL Statements panel In [TiDB Dashboard](dashboard/dashboard-overview.md), navigate to SQL Statements panel, which helps us identify the following: @@ -88,9 +88,10 @@ In [TiDB Dashboard](dashboard/dashboard-overview.md), navigate to SQL Statements 3. Clicking on each SQL statement allows us to delve deeper into the `EXPLAIN ANALYZE` results. SQL statements are normalized as templates, where literals and bind variables are replaced by '?'. This normalization and sorting process allows you to quickly pinpoint the most resource-intensive queries that may require optimization. + ![sql-statements-default](/media/sql-tuning/sql-statements-default.png) -### Slow Queries Panel Default Display +### Slow Queries panel default display On Slow Queries panel, we can find: @@ -98,9 +99,10 @@ On Slow Queries panel, we can find: 2. The SQL query that reads the most data from TiKV. 3. The `EXPLAIN ANALYZE` output from drilling down the SQL statement by clicking it. 4. Please note that on the Slow Queries panel, we cannot get the frequency of the SQL statement execution. Once the execution elapsed time exceeds [`tidb_slow_log_threshold`](/tidb-configuration-file.md#tidb_slow_log_threshold) for single instance, the query is then listed on the Slow Queries panel. + ![slow-query-default](/media/sql-tuning/slow-query-default.png) -## Other Tools for Identifying Top SQL +## Other tools for identifying Top SQL In addition to TiDB Dashboard, there are several other tools available to identify resource-intensive SQL queries: @@ -110,7 +112,7 @@ Each tool offers unique insights and can be valuable for different analysis scen - Logs: [slow query log](/identify-slow-queries.md) and [expensive queries in TiDB log](/identify-expensive-queries.md) - Views: [cluster_statements_summary view](/statement-summary-tables.md##the-cluster-tables-for-statement-summary) and [cluster_processlist view](/information-schema/information-schema-processlist.md#cluster_processlist) -## Gathering Data on the SQL Identified +## Gathering data on the SQL identified For the top SQL statements identified, you can use [PLAN REPLAYER](/sql-plan-replayer.md) to capture and save the on-site information of a TiDB cluster. This tool allows you to recreate the execution environment for further analysis. The syntax for exporting SQL information is as follows: @@ -120,7 +122,7 @@ PLAN REPLAYER DUMP EXPLAIN [ANALYZE] [WITH STATS AS OF TIMESTAMP expression] sql Use `EXPLAIN ANALYZE` whenever possible, as it captures actual execution information in addition to the execution plan. This provides more accurate insights into query performance. -# SQL Tuning Guide +# SQL tuning guide This guide focuses on providing actionable advice for beginners looking to optimize their SQL queries in TiDB. By following these best practices, you can ensure better query performance and SQL Tuning. We'll cover below topic: @@ -137,7 +139,7 @@ This guide focuses on providing actionable advice for beginners looking to optim - SQL Tuning with a Composite Index for Efficient Filtering and Sorting - When to Use TiFlash: A Simple Guide -## Query Processing Workflow +## Query processing workflow The client sends a SQL statement to the protocol layer of TiDB server. The protocol layer is responsible for handling the connection between TiDB server and the client, receiving SQL statement from the client, and returning data to the client. @@ -153,7 +155,7 @@ To the right of the protocol layer is the optimizer of TiDB server, which is res ![workflow](/media/sql-tuning/workflow-tiflash.png) -## Optimizer Fundamentals +## Optimizer fundamentals TiDB uses a cost-based optimizer (CBO) to determine the most efficient execution plan for a SQL statement. This optimizer evaluates different execution strategies and chooses the one with the lowest estimated cost. The cost is influenced by factors such as: @@ -172,7 +174,7 @@ Based on the input, The cost model will produce the execution plan, which includ The optimizer is as good as the information it receives. Therefore, ensuring up-to-date statistics and well-designed indexes is critical. -## Statistics Management +## Statistics management Statistics are essential to the TiDB optimizer. TiDB uses statistics as input to the optimizer to estimate the number of rows processed in each plan step for a SQL statement. @@ -251,7 +253,7 @@ To lock the statistics for a table, you can use the following statement [`LOCK S for more detail about statistics, please refer to [statistics](/statistics.md). -## How TiDB Builds A Execution Plan +## How TiDB builds a execution plan An SQL statement undergoes optimization primarily in the optimizer through three stages: @@ -259,7 +261,7 @@ An SQL statement undergoes optimization primarily in the optimizer through three - Logical Transformation - Cost-based Optimization -### Pre-Processing +### Pre-processing The main actions in the pre-processing stage it to determine if the SQL statement can be executed by using [`Point_Get`](/explain-indexes#point_get-and-batch_point_get) or [`Batch_Point_Get`](/explain-indexes#point_get-and-batch_point_get). `Point_Get` or `Batch_Point_Get` means using a primary or unique key, to directly read from TiKV, by exact key lookup. If a plan is identified as `Point_Get` or `Batch_Point_Get`, optimizer will skip the logical transformation and cost-based optimization, since the exact key read will be the best way to access the row. @@ -277,13 +279,13 @@ explain SELECT id, name FROM emp WHERE id = 901; +-------------+---------+------+---------------+---------------+ ``` -### Logical Transformation +### Logical transformation The purpose of logical Transformation is to optimize the execution of statements based on the characteristics of SELECT list, WHERE predicates, and other predicates in SQL queries. It generates a logical execution plan to annotate and rewrite the query. This logical plan is then passed to the Cost-Based Optimization. The optimization rules are such as column pruning, partition pruning, join reorder etc. Since this step is rule-based and automated by the query optimizer, in most cases it usually does not require manual adjustments. More Detail for Logical Transformation: [SQL Logical Optimization](/sql-logical-optimization.md). -### Cost-Based Optimization +### Cost-Based optimization TiDB uses statistics as input to the optimizer to estimate the number of rows processed in each plan step for a SQL statement, and associates a cost with each plan step. The Cost-Based Optimization estimates the cost of each available plan choice, including index accesses and join methods, and produces a cost for each available plan. The optimizer then picks the execution plan with the lowest overall cost. @@ -303,11 +305,11 @@ This distribution of the physical plan allows the different components to collab More Detail for Cost-Based Optimization: [SQL Physical Optimization](/sql-physical-optimization.md). -## Understanding Execution Plans +## Understanding execution plans The execution plan represents the steps TiDB will follow to execute a SQL query. In this section, we will learn how to display and read the execution plan. -### Generating and Displaying Execution Plans +### Generating and displaying execution plans Beside access the execution plan information through TiDB Dashboard, TiDB provides a `EXPLAIN` statement to display the execution plan for a SQL query. Here's an example of using `EXPLAIN`: @@ -383,7 +385,7 @@ FROM ( +-----------------------------------------+.+---------+-----------+---------------------------+----------------------------------------------------------------+.+-----------+---------+ ``` -### Reading Execution Plans: First Child First +### Reading execution plans: first child first To understand why SQL queries run slowly, it's very important to know how to read Execution Plans. The main rule for reading an execution plan is "first child first – recursive descent". Each operator in the plan produces a set of rows, and the execution order determines how these rows flow through the plan tree. @@ -414,7 +416,7 @@ EXPLAIN SELECT COUNT(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00 5 rows in set (0.00 sec) ``` -Let's apply the "first child first – recursive descent" rule to the second plan. In the below example begin from the top to bottom, by looking at the `IndexRangeScan_47` (the first leaf of the plan tree). For the table `stars`, the optimizer only needs to select the column `name` and `id`, the two columns can be met by the index `name(name)`. So for the table `star`, the root reader is `IndexReader_48`, rather than a `TableReader`. The join method between `stars` and `planets` is a hash join, which is marked as `HashJoin_44`. The data access method on `planets` is a `TableFullScan_45`. After the join, the `TopN_26` and `TOPN_19` are to implement the `ORDER BY` and `LIMIT` clauses correspondingly. The final operator `Projection_16` is to implement the column projection for `t5.name`. +Let's apply the "first child first – recursive descent" rule to the second plan. In the below example begin from the top to bottom, by looking at the `IndexRangeScan_47` (the first leaf of the plan tree). For the table `stars`, the optimizer only needs to select the column `name` and `id`, the two columns can be met by the index `name(name)`. So for the table `star`, the root reader is `IndexReader_48`, rather than a `TableReader`. The join method between `stars` and `planets` is a hash join, which is marked as `HashJoin_44`. The data access method on `planets` is a `TableFullScan_45`. After the join, the `TopN_26` and `TopN_19` are to implement the `ORDER BY` and `LIMIT` clauses correspondingly. The final operator `Projection_16` is to implement the column projection for `t5.name`. Here is the query statement: @@ -470,7 +472,7 @@ To read this plan: This traversal ensures that each operator's inputs are processed before the operator itself, allowing for efficient execution of the query plan. -### Identifying and Understanding Bottlenecks in Execution Plans +### Identifying and understanding bottlenecks in execution plans When reading the execution plan, it's crucial to compare the `actRows` (actual rows) with the `estRows` (estimated rows) to assess the accuracy of the optimizer's estimations. A significant discrepancy between these values may indicate that the optimizer's statistics are outdated or inaccurate, potentially leading to suboptimal query plans. @@ -544,7 +546,7 @@ This optimized plan demonstrates the importance of accurate statistics and prope More Detail for understanding execution plans : [TiDB Query Execution Plan Overview](/explain-overview.md) and [EXPLAIN Walkthrough](/explain-walkthrough.md). -## Index Strategy in TiDB +## Index strategy in TiDB TiDB is a distributed SQL database that completely decouples the SQL layer (TiDB Server) from the storage layer (TiKV). Unlike traditional databases, TiDB does not have a buffer pool to cache data at the compute node. As a result, the performance of SQL queries and the TiDB cluster is closely tied to the number of key-value (KV) RPC requests that need to be processed. Typical KV RPC are `Point_Get`, `Batch_Point_Get`, and Coprocessor. @@ -556,7 +558,7 @@ In TiDB, leveraging indexes effectively is crucial for performance tuning, as it This section explains the general index strategy and the cost of indexing. Then it showcases three practical examples that illustrate effective indexing strategies in TiDB, focusing on the use of composite and covering indexes for sql tuning. -### Composite Index Strategy Guidelines +### Composite index strategy guidelines When creating a composite index, it's essential to follow a specific order for the columns to ensure optimal performance. This order is crucial because it directly impacts how efficiently the index can filter and sort data. @@ -577,7 +579,7 @@ Here is the recommended order for the columns in the index: 4. Include columns in the SELECT list or aggregation to fully utilize a covering index. -### The Cost of Indexing +### The cost of indexing While indexes can significantly improve query performance, they also come with costs that should be carefully considered: @@ -603,7 +605,7 @@ Best Practice: - Consider the write/read ratio of your workload when designing indexes -### SQL Tuning with a Covering Index +### SQL tuning with a covering index A covering index is designed to include all columns referenced in the filter and select clauses. The query below requires an index lookup of 2597411 rows, taking 46.4 seconds to execute. TiDB needs to dispatch 67 cop tasks for the index range scan on logs_idx, identified as `IndexRangeScan_11`, and 301 cop tasks for table access via `TableRowIDScan_12`. By utilizing a covering index, the index lookup can be avoided, leading to improved performance. @@ -666,7 +668,7 @@ New plan: +-------------------------------+------------+---------+-----------+---------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------+ ``` -### SQL Tuning with a Composite Index Involving Sorting +### SQL tuning with a composite index involving sorting When optimizing SQL queries, especially those that include an `ORDER BY` clause, it is beneficial to create a composite index that encompasses both the filtering and sorting columns. This approach allows the database engine to efficiently access the required data while maintaining the desired order. @@ -725,7 +727,7 @@ New plan: +----------------------------------+---------+---------+-----------+----------------------------------------------+----------------------------------------------+----------------------------------------------------+ ``` -### SQL Tuning with Composite Indexes for Efficient Filtering and Sorting +### SQL tuning with composite indexes for efficient filtering and sorting The original query below took 11 minutes and 9 seconds to complete, which is an extremely long execution time for a query that only needs to return 101 rows. This poor performance can be attributed to several factors: @@ -805,7 +807,7 @@ New plan: ``` -## When to Use TiFlash: A Simple Guide +## When to use TiFlash: a simple guide This section explains the best scenarios for using TiFlash in TiDB. TiFlash is optimized for analytical queries that involve complex calculations, aggregations, and large dataset scans, thanks to its columnar storage format. Here’s when to consider TiFlash: @@ -816,7 +818,7 @@ This section explains the best scenarios for using TiFlash in TiDB. TiFlash is o Using TiFlash strategically can enhance query performance and optimize resource usage in TiDB for data-intensive, analytical queries. Here are two use case of TiFlash -### Analytical Query +### Analytical query Take the TPC-H Query 14 as an example. The original query requires joining the order_line and item tables. The plan for executing this query on the TiKV storage engine takes 21.1 seconds. The plan for executing the same query using TiFlash MPP mode takes only 1.41 seconds, which is 15 times faster than the TiKV plan. @@ -872,20 +874,20 @@ TiFlash plan +--------------------------------------------+-------------+----------+--------------+----------------+--------------------------------------+ ``` -### SaaS Arbitrary Filtering Workloads +### SaaS arbitrary filtering workloads #### Overview In SaaS applications, it's common to structure tables with composite primary keys that include tenant identification. Let's look at a typical example where TiFlash can significantly improve query performance. -#### Case Study: Multi-tenant Data Access +#### Case study: multi-tenant data access Consider a table design with a composite primary key: `(tenantId, objectTypeId, objectId)`. A common query pattern is to: - Fetch the first N records for a specific tenant and object type, and applying random filters across hundreds or thousands of other columns, Making it impractical to create indexes for every possible filter combination. There is a potential sort operator after the filter as well. - Get the total count of records matching these criterias -#### Performance Comparison +#### Performance comparison - TiKV Plan: When executing this query on TiKV storage engine, it takes 2 minutes 38.6 seconds. `TableRangeScan` need to send 5121 cop tasks since data is spread over 5121 regions. - TiFlash Plan: The same query on TiFlash MPP engine takes only 3.44 seconds - almost 46 times faster. Since data in TiFlash is sorted by primary key, queries filtered by the primary key's prefix will also use a `TableRangeScan` instead of a full table scan. Compared to TiKV, TiFlash only uses 2 mpp tasks. @@ -953,7 +955,7 @@ TiFlash plan +--------------------------------+-----------+---------+--------------+--------------------+-----------------------------------------------------+ ``` -#### Query Routing between TiKV and TiFlash +#### Query routing between TiKV and TiFlash After enabling TiFlash replicas for tables with large amounts of multi-tenant data, the optimizer can make choices to serve the query on TiKV or TiFlash storage engine based on the row count: From 15574bca9d29ba80cc2aa567df345b502b387846 Mon Sep 17 00:00:00 2001 From: dbsid Date: Thu, 12 Dec 2024 17:16:30 +0800 Subject: [PATCH 45/46] adjust TOC in Tuning guide --- TOC.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/TOC.md b/TOC.md index c4ddb8fe8569f..f47ac6b961f9b 100644 --- a/TOC.md +++ b/TOC.md @@ -309,11 +309,12 @@ - [Performance Tuning Overview](/performance-tuning-overview.md) - [Configure TiDB for Optimal Performance](/tidb-performance-tuning-config.md) - [A Practical Guide For SQL Tuning](/sql-tuning-best-practice.md) - - [Performance Analysis and Tuning](/performance-tuning-methods.md) - - [Performance Tuning Practices for OLTP Scenarios](/performance-tuning-practices.md) - - [TiFlash Performance Analysis Methods](/tiflash-performance-tuning-methods.md) - - [TiCDC Performance Analysis Methods](/ticdc-performance-tuning-methods.md) - - [Latency Breakdown](/latency-breakdown.md) + - Tuning by Metrics + - [Performance Analysis and Tuning](/performance-tuning-methods.md) + - [Performance Tuning Practices for OLTP Scenarios](/performance-tuning-practices.md) + - [TiFlash Performance Analysis Methods](/tiflash-performance-tuning-methods.md) + - [TiCDC Performance Analysis Methods](/ticdc-performance-tuning-methods.md) + - [Latency Breakdown](/latency-breakdown.md) - [TiDB Best Practices on Public Cloud](/best-practices-on-public-cloud.md) - Configuration Tuning - [Tune Operating System Performance](/tune-operating-system.md) From 74f212781cdeb2057429ede651d91b5a3ca85c22 Mon Sep 17 00:00:00 2001 From: dbsid Date: Fri, 13 Dec 2024 16:21:00 +0800 Subject: [PATCH 46/46] fix ci --- sql-tuning-best-practice.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/sql-tuning-best-practice.md b/sql-tuning-best-practice.md index e5e65b868466b..d2b6daa17dd41 100644 --- a/sql-tuning-best-practice.md +++ b/sql-tuning-best-practice.md @@ -604,7 +604,6 @@ Best Practice: - Regularly review index usage statistics via [TIDB_INDEX_USAGE](/information-schema/information-schema-tidb-index-usage.md) - Consider the write/read ratio of your workload when designing indexes - ### SQL tuning with a covering index A covering index is designed to include all columns referenced in the filter and select clauses. The query below requires an index lookup of 2597411 rows, taking 46.4 seconds to execute. TiDB needs to dispatch 67 cop tasks for the index range scan on logs_idx, identified as `IndexRangeScan_11`, and 301 cop tasks for table access via `TableRowIDScan_12`. By utilizing a covering index, the index lookup can be avoided, leading to improved performance. @@ -829,8 +828,8 @@ Here is the query statement: ```sql select 100.00 * sum(case when i_data like 'PR%' then ol_amount else 0 end) / (1+sum(ol_amount)) as promo_revenue -from order_line, item -where ol_i_id = i_id and ol_delivery_d >= '2007-01-02 00:00:00.000000' and ol_delivery_d < '2030-01-02 00:00:00.000000'; +from order_line, item +where ol_i_id = i_id and ol_delivery_d >= '2007-01-02 00:00:00.000000' and ol_delivery_d < '2030-01-02 00:00:00.000000'; ``` TiKV plan

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