Demo refactor updates

demo/web/src/main/notebooks/00 The Deephaven IDE.md

@@ -44,7 +44,7 @@ static_table = newTable(
 ```python
 from deephaven.TableTools import timeTable
 import random
-updating_table = timeTable('00:00:00.400').updateView("Row = i", "Some_Int =  random.randint(0,100)").reverse()
+updating_table = timeTable('00:00:00.400').updateView("Row = i", "Some_Int = (int)random.randint(0,100)").reverse()
 ```
 
 

demo/web/src/main/notebooks/01 Tables, Updates, and the Engine.md

@@ -9,16 +9,16 @@ You can quickly see streaming data in a UI and do table operations, interactivel
 For example, you can listen to a Kafka stream of cryptocurrency trades sourced from their native exchanges (like the ones below, built using the [XChange library](https://github.com/knowm/XChange)).
 
 ```python
-from deephaven import KafkaTools as kt
+from deephaven import ConsumeKafka as ck
 
 def get_trades_stream():
- return kt.consumeToTable(
+ return ck.consumeToTable(
  { 'bootstrap.servers' : 'demo-kafka.c.deephaven-oss.internal:9092',
  'schema.registry.url' : 'http://demo-kafka.c.deephaven-oss.internal:8081' },
  'io.deephaven.crypto.kafka.TradesTopic',
- key = kt.IGNORE,
- value = kt.avro('io.deephaven.crypto.kafka.TradesTopic-io.deephaven.crypto.Trade'),
- offsets=kt.ALL_PARTITIONS_SEEK_TO_END,
+ key = ck.IGNORE,
+ value = ck.avro('io.deephaven.crypto.kafka.TradesTopic-io.deephaven.crypto.Trade'),
+ offsets=ck.ALL_PARTITIONS_SEEK_TO_END,
  table_type='append')
 
 trades_stream = get_trades_stream()
@@ -29,9 +29,6 @@ trades_stream = get_trades_stream()
 To keep the most recent ticks within view, you could sort the table descending by timestamp. Alternatively, you can reverse the table.
 
 ```python
-# Not doing this:
-# t = t.sortDescending("Timestamp")
-
 trades_stream = trades_stream.reverse()
 ```
 \
@@ -83,14 +80,18 @@ row_count_by_instrument = trades_stream_cleaner.countBy("Tot_Rows", "Instrument"
 Counts are informative, but often you'll be interested in other aggregations. The script below shows both how to [bin data by time](https://deephaven.io/core/docs/reference/cheat-sheets/datetime-cheat-sheet/#downsampling-temporal-data-via-time-binning) and to [do multiple aggregations](https://deephaven.io/core/docs/how-to-guides/combined-aggregations/).
 
 ```python
-from deephaven import ComboAggregateFactory as caf
+from deephaven import Aggregation as agg, as_list
+
+agg_list = as_list([
+ agg.AggCount("Trade_Count"),
+ agg.AggSum("Total_Size = Size"),
+ agg.AggAvg("Avg_Size = Size", "Avg_Price = Price"),
+ agg.AggMin("Low_Price = Price"),
+ agg.AggMax("High_Price = Price")
+])
+
 multi_agg = trades_stream_cleaner.updateView("TimeBin = upperBin(KafkaTimestamp, MINUTE)")\
- .by(caf.AggCombo(
- caf.AggCount("Trade_Count"),
- caf.AggSum("Total_Size = Size"),
- caf.AggAvg("Avg_Size = Size", "Avg_Price = Price"),
- caf.AggMin("Low_Price = Price"),
- caf.AggMax("High_Price = Price")),"TimeBin", "Instrument")\
+ .aggBy(agg_list, "TimeBin", "Instrument")\
  .sortDescending("TimeBin", "Trade_Count")\
  .formatColumnWhere("Instrument", "Instrument = `BTC/USD`", "CYAN")
 ```

demo/web/src/main/notebooks/02 Stream and Batch Together.md

@@ -12,16 +12,16 @@ Below you’ll do calculations and aggregations on stream and batch data using i
 First, hook up a Kafka stream. (This is the same script from the first notebook.) Our [how-to guide](https://deephaven.io/core/docs/how-to-guides/kafka-stream/) provides detail on the integration.
 
 ```python
-from deephaven import KafkaTools as kt
+from deephaven import ConsumeKafka as ck
 
 def get_trades_stream():
- return kt.consumeToTable(
+ return ck.consumeToTable(
  { 'bootstrap.servers' : 'demo-kafka.c.deephaven-oss.internal:9092',
  'schema.registry.url' : 'http://demo-kafka.c.deephaven-oss.internal:8081' },
  'io.deephaven.crypto.kafka.TradesTopic',
- key = kt.IGNORE,
- value = kt.avro('io.deephaven.crypto.kafka.TradesTopic-io.deephaven.crypto.Trade'),
- offsets=kt.ALL_PARTITIONS_SEEK_TO_END,
+ key = ck.IGNORE,
+ value = ck.avro('io.deephaven.crypto.kafka.TradesTopic-io.deephaven.crypto.Trade'),
+ offsets=ck.ALL_PARTITIONS_SEEK_TO_END,
  table_type='append')
 
 trades_stream = get_trades_stream()
@@ -64,8 +64,8 @@ Let's return to our crypto data.
 Read in a CSV of batch crypto data sourced on 09/22/2021.
 
 ```python
-from deephaven.TableTools import readCsv
-trades_batch_view = readCsv("/data/large/crypto/CryptoTrades_20210922.csv")
+from deephaven import read_csv
+trades_batch_view = read_csv("/data/large/crypto/CryptoTrades_20210922.csv")
 ```
 
 \
@@ -93,15 +93,26 @@ The following scripts will demonstrate much the same with two examples:
 
 ```python
 # the table decoration
-from deephaven.DBTimeUtils import formatDate
+from deephaven.DateTimeUtils import formatDate
 
 add_column_streaming = trades_stream_view.updateView("Date = formatDate(KafkaTimestamp, TZ_NY)")
 add_column_batch = trades_batch_view .updateView("Date = formatDate(Timestamp, TZ_NY)")
 
 # the table aggregation
-from deephaven import ComboAggregateFactory as caf
-agg_streaming = add_column_streaming.by(caf.AggCombo(caf.AggFirst("Price"), caf.AggAvg("Avg_Price = Price")), "Date", "Exchange", "Instrument")
-agg_batch = add_column_batch .by(caf.AggCombo(caf.AggFirst("Price"), caf.AggAvg("Avg_Price = Price")), "Date", "Exchange", "Instrument")
+from deephaven import Aggregation as agg, as_list
+
+agg_list = as_list([
+ agg.AggFirst("Price"),
+ agg.AggAvg("Avg_Price = Price"),
+])
+
+agg_streaming = add_column_streaming.aggBy(
+ agg_list, "Date", "Exchange", "Instrument"
+)
+
+agg_batch = add_column_batch.aggBy(
+ agg_list, "Date", "Exchange", "Instrument"
+)
 ```
 
 \

demo/web/src/main/notebooks/03 Kafka Stream vs Append.md

@@ -17,7 +17,7 @@ Start by importing some requisite packages. There is documentation on [installin
 [ComboAggregateFactory](https://deephaven.io/core/docs/reference/table-operations/group-and-aggregate/AggCombo/), [emptyTable](https://deephaven.io/core/docs/how-to-guides/empty-table/#related-documentation), and [merge](https://deephaven.io/core/docs/how-to-guides/merge-tables/#merge-tables).
 
 ```python
-from deephaven import KafkaTools as kt, ComboAggregateFactory as caf
+from deephaven import ConsumeKafka as ck, Aggregation as agg, combo_agg
 from deephaven.TableTools import emptyTable, merge
 ```
 
@@ -32,12 +32,12 @@ This demo will demonstrate the impact of choices related to `offsets` and `table
 
 ```python
 def get_trades(*, offsets, table_type):
- return kt.consumeToTable(
+ return ck.consumeToTable(
  { 'bootstrap.servers' : 'demo-kafka.c.deephaven-oss.internal:9092',
  'schema.registry.url' : 'http://demo-kafka.c.deephaven-oss.internal:8081' },
  'io.deephaven.crypto.kafka.TradesTopic',
- key = kt.IGNORE,
- value = kt.avro('io.deephaven.crypto.kafka.TradesTopic-io.deephaven.crypto.Trade'),
+ key = ck.IGNORE,
+ value = ck.avro('io.deephaven.crypto.kafka.TradesTopic-io.deephaven.crypto.Trade'),
  offsets=offsets,
  table_type=table_type)
 ```
@@ -50,7 +50,7 @@ In this demo, imagine you want to start your Kafka feed "1 million events ago" (
 Create a Deephaven table that listens to current records (-- i.e. crypto trades happening now).
 
 ```python
-latest_offset = get_trades(offsets=kt.ALL_PARTITIONS_SEEK_TO_END, table_type='stream')
+latest_offset = get_trades(offsets=ck.ALL_PARTITIONS_SEEK_TO_END, table_type='stream')
 ```
 
 \
@@ -100,9 +100,13 @@ Define a [table aggregation function](https://deephaven.io/core/docs/reference/t
 
 ```python
 def trades_agg(table):
- return table.by(caf.AggCombo(caf.AggCount("Trade_Count"),caf.AggSum("Total_Size = Size")),"Exchange", "Instrument")\
- .sort("Exchange", "Instrument")\
- .formatColumnWhere("Instrument", "Instrument.startsWith(`BTC`)", "IVORY")
+ agg_list = combo_agg([
+ agg.AggCount("Trade_Count"),
+ agg.AggSum("Total_Size = Size"),
+ ])
+ return table.aggBy(agg_list, "Exchange", "Instrument").\
+ sort("Exchange", "Instrument").\
+ formatColumnWhere("Instrument", "Instrument.startsWith(`BTC`)", "IVORY")
 ```
 
 \