From 3bd54297a6f2965cd865eb309e96109a395d624c Mon Sep 17 00:00:00 2001 From: Hao Xu Date: Tue, 10 Jun 2025 21:32:45 -0700 Subject: [PATCH 1/4] add compute example Signed-off-by: Hao Xu --- .gitignore | 4 +- module_3_db/feature_repo/feature_store.yaml | 2 + module_3_db/sample_db_notebook.ipynb | 8807 ++++++++++++++++- module_5_compute_engine/README.md | 312 + module_5_compute_engine/architecture.png | Bin 0 -> 167522 bytes module_5_compute_engine/compute_example.ipynb | 334 + .../feature_repo/data_sources.py | 25 + .../feature_repo/entities.py | 11 + .../feature_repo/feature_services.py | 9 + .../feature_repo/feature_store.yaml | 33 + .../feature_repo/features.py | 30 + module_5_compute_engine/orchestrate.png | Bin 0 -> 222627 bytes module_5_compute_engine/poetry.lock | 4025 ++++++++ module_5_compute_engine/pyproject.toml | 24 + 14 files changed, 13614 insertions(+), 2 deletions(-) create mode 100644 module_5_compute_engine/README.md create mode 100644 module_5_compute_engine/architecture.png create mode 100644 module_5_compute_engine/compute_example.ipynb create mode 100644 module_5_compute_engine/feature_repo/data_sources.py create mode 100644 module_5_compute_engine/feature_repo/entities.py create mode 100644 module_5_compute_engine/feature_repo/feature_services.py create mode 100644 module_5_compute_engine/feature_repo/feature_store.yaml create mode 100644 module_5_compute_engine/feature_repo/features.py create mode 100644 module_5_compute_engine/orchestrate.png create mode 100644 module_5_compute_engine/poetry.lock create mode 100644 module_5_compute_engine/pyproject.toml diff --git a/.gitignore b/.gitignore index ad641bf..21478e6 100644 --- a/.gitignore +++ b/.gitignore @@ -12,4 +12,6 @@ terraform.tfstate.backup **/derby.log **/metastore_db/* .env -.idea \ No newline at end of file +.idea +.venv +**/.ipynb_checkpoints diff --git a/module_3_db/feature_repo/feature_store.yaml b/module_3_db/feature_repo/feature_store.yaml index 4451821..218634e 100644 --- a/module_3_db/feature_repo/feature_store.yaml +++ b/module_3_db/feature_repo/feature_store.yaml @@ -15,3 +15,5 @@ offline_store: spark.sql.parser.quotedRegexColumnNames: "true" spark.sql.session.timeZone: "UTC" entity_key_serialization_version: 2 +batch_engine: + diff --git a/module_3_db/sample_db_notebook.ipynb b/module_3_db/sample_db_notebook.ipynb index da03202..5b4b686 100644 --- a/module_3_db/sample_db_notebook.ipynb +++ b/module_3_db/sample_db_notebook.ipynb @@ -1 +1,8806 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"0c4646a7-272d-44ec-95fe-3480a267e173","showTitle":false,"title":""}},"outputs":[],"source":["%pip install feast[spark,aws,redis]"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"d47268f0-01e8-442c-be33-97b11f444aaf","showTitle":false,"title":""}},"source":["## Try fetching data from a data source"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"8a29f106-0008-462c-876e-1dfb13ecff08","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["
user_idtimestampamt_sum_1d_10mamt_mean_1d_10m
user_2683081518772022-04-07T22:12:37.304+00001.791.79
user_2683081518772022-04-07T22:23:06.566+000010.8700000000000015.4350000000000005
user_2683081518772022-04-07T22:26:15.216+000016.855.616666666666667
user_2683081518772022-04-07T22:31:28.924+0000101.8825.47
user_2683081518772022-04-07T22:31:41.513+0000109.821.96
user_2683081518772022-04-07T22:39:28.687+0000214.1335.68833333333333
user_2683081518772022-04-07T22:39:42.791+0000275.1739.31
user_2683081518772022-04-07T22:39:48.833+0000355.0144.37625
user_2683081518772022-04-07T22:40:42.840+0000466.3251.81333333333333
user_2683081518772022-04-07T22:41:20.686+0000491.9549.195
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"aggData":[],"aggError":"","aggOverflow":false,"aggSchema":[],"aggSeriesLimitReached":false,"aggType":"","arguments":{},"columnCustomDisplayInfos":{},"data":[["user_268308151877","2022-04-07T22:12:37.304+0000",1.79,1.79],["user_268308151877","2022-04-07T22:23:06.566+0000",10.870000000000001,5.4350000000000005],["user_268308151877","2022-04-07T22:26:15.216+0000",16.85,5.616666666666667],["user_268308151877","2022-04-07T22:31:28.924+0000",101.88,25.47],["user_268308151877","2022-04-07T22:31:41.513+0000",109.8,21.96],["user_268308151877","2022-04-07T22:39:28.687+0000",214.13,35.68833333333333],["user_268308151877","2022-04-07T22:39:42.791+0000",275.17,39.31],["user_268308151877","2022-04-07T22:39:48.833+0000",355.01,44.37625],["user_268308151877","2022-04-07T22:40:42.840+0000",466.32,51.81333333333333],["user_268308151877","2022-04-07T22:41:20.686+0000",491.95,49.195]],"datasetInfos":[],"dbfsResultPath":null,"isJsonSchema":true,"metadata":{},"overflow":false,"plotOptions":{"customPlotOptions":{},"displayType":"table","pivotAggregation":null,"pivotColumns":null,"xColumns":null,"yColumns":null},"removedWidgets":[],"schema":[{"metadata":"{}","name":"user_id","type":"\"string\""},{"metadata":"{}","name":"timestamp","type":"\"timestamp\""},{"metadata":"{}","name":"amt_sum_1d_10m","type":"\"double\""},{"metadata":"{}","name":"amt_mean_1d_10m","type":"\"double\""}],"type":"table"}},"output_type":"display_data"}],"source":["%sql\n","SELECT \n"," user_id,\n"," to_timestamp(timestamp) as timestamp,\n"," SUM(amt) OVER (PARTITION BY user_id ORDER BY to_timestamp(timestamp) RANGE BETWEEN INTERVAL 1 day PRECEDING AND CURRENT ROW) as amt_sum_1d_10m,\n"," AVG(amt) OVER (PARTITION BY user_id ORDER BY to_timestamp(timestamp) RANGE BETWEEN INTERVAL 1 day PRECEDING AND CURRENT ROW) as amt_mean_1d_10m\n","FROM demo_fraud_v2.transactions \n","WHERE \n"," partition_0 = \"2022\" AND\n"," partition_1 = \"04\"\n","limit 10"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"37baef9e-ffac-4cf9-ab6c-778e0321d544","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from feast import FeatureStore, RepoConfig\n","from feast.repo_config import RegistryConfig\n","from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig\n","from feast.infra.offline_stores.contrib.spark_offline_store.spark import SparkOfflineStoreConfig\n","import warnings\n","warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n","warnings.filterwarnings(\"ignore\", category=RuntimeWarning) \n","\n","repo_config = RepoConfig(\n"," registry=RegistryConfig(\n"," registry_type=\"sql\", \n"," path=\"postgresql://postgres:mysecretpassword@[YOUR-RDS-ENDPOINT:PORT]:5432/feast\"\n"," ),\n"," project=\"feast_demo\",\n"," provider=\"local\",\n"," offline_store=SparkOfflineStoreConfig(\n"," spark_conf={\n"," \"spark.ui.enabled\": \"false\",\n"," \"spark.eventLog.enabled\": \"false\",\n"," \"spark.sql.catalogImplementation\": \"hive\",\n"," \"spark.sql.parser.quotedRegexColumnNames\": \"true\",\n"," \"spark.sql.session.timeZone\": \"UTC\"\n"," }\n"," ),\n"," online_store=DynamoDBOnlineStoreConfig(region=\"us-west-1\"),\n"," entity_key_serialization_version=2\n",")\n","store = FeatureStore(config=repo_config)"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"662c84bf-b655-4da2-8f59-4bf82b92241c","showTitle":false,"title":""}},"source":["## Managing data pipelines\n","\n","### Part 1: Moving the latest feature values into a low latency store"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"83ad34fa-cbd3-4100-8c01-40720ddbc14e","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n","\n","user_transaction_amount_metrics:\n","Pulling latest features from spark offline store\n","\r 0%| | 0/46 [00:00<?, ?it/s]\r 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n\nuser_transaction_amount_metrics:\nPulling latest features from spark offline store\n\r 0%| | 0/46 [00:00<?, ?it/s]\r 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from datetime import datetime\n","store.materialize(\n"," start_date=datetime(2022,4,20), \n"," end_date=datetime(2022,4,21), \n",")"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"1921ca3d-011a-4171-9d7b-9c55e5464f01","showTitle":false,"title":""}},"source":["### Part 2: Computing + pushing streaming feature values"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"b45f5951-bf01-48ae-8b76-3db0390508a8","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from datetime import timedelta\n","from pyspark.sql.functions import col, from_json, from_utc_timestamp, when\n","from pyspark.sql.types import StructType, StructField, StringType, DoubleType, TimestampType, BooleanType, IntegerType\n","\n","def raw_data_deserialization(df):\n"," payload_schema = StructType([\n"," StructField('user_id', StringType(), False),\n"," StructField('transaction_id', StringType(), False),\n"," StructField('category', StringType(), False),\n"," StructField('amt', StringType(), False),\n"," StructField('is_fraud', StringType(), False),\n"," StructField('merchant', StringType(), False),\n"," StructField('merch_lat', StringType(), False),\n"," StructField('merch_long', StringType(), False),\n"," StructField('timestamp', StringType(), False),\n"," ])\n","\n"," return (\n"," df.selectExpr('cast (data as STRING) jsonData')\n"," .select(from_json('jsonData', payload_schema).alias('payload'))\n"," .select(\n"," col('payload.user_id').alias('user_id'),\n"," col('payload.transaction_id').alias('transaction_id'),\n"," col('payload.category').alias('category'),\n"," col('payload.amt').cast('double').alias('amt'),\n"," col('payload.is_fraud').cast('long').alias('is_fraud'),\n"," col('payload.merchant').alias('merchant'),\n"," col('payload.merch_lat').cast('double').alias('merch_lat'),\n"," col('payload.merch_long').cast('double').alias('merch_long'),\n"," from_utc_timestamp('payload.timestamp', 'UTC').alias('timestamp')\n"," )\n"," )\n","\n","def ingest_and_deserialize_kinesis_df():\n"," options = {\n"," \"streamName\": \"[REDACTED STREAM NAME]\",\n"," \"roleArn\": \"[REDACTED ROLE]\",\n"," \"region\": \"us-west-2\",\n"," \"shardFetchInterval\": \"30s\",\n"," \"initialPosition\": \"latest\"\n"," }\n"," reader = spark.readStream.format(\"kinesis\").options(**options)\n"," df = reader.load()\n"," df = raw_data_deserialization(df)\n"," watermark = \"{} seconds\".format(timedelta(hours=24).seconds)\n"," df = df.withWatermark(\"timestamp\", watermark)\n"," return df\n","\n","# 1) Ingest and deserialize streaming events from Kinesis stream\n","df = ingest_and_deserialize_kinesis_df()"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"30c5835d-64fd-4439-b99c-a3c58ac7673a","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from pyspark.sql.functions import *\n","import pandas as pd\n","\n","# 2) Aggregate feature values\n","stream_agg = (\n"," df.withWatermark(\"timestamp\", \"1 second\") \n"," .groupBy(\"user_id\", window(timeColumn=\"timestamp\", windowDuration=\"1 day\", slideDuration=\"10 minutes\"))\n"," .agg(\n"," sum(\"amt\").alias(\"amt_sum_1d_10m\"),\n"," avg(\"amt\").alias(\"amt_mean_1d_10m\")\n"," )\n"," .select(\"user_id\", col(\"window.end\").alias(\"timestamp\"), \"amt_sum_1d_10m\", \"amt_mean_1d_10m\")\n",")\n","\n","# 3) Push transformed features into Feast\n","from feast import FeatureStore\n","store = FeatureStore(config=repo_config)\n","\n","def send_to_feast(df, epoch):\n"," pandas_df: pd.DataFrame = df.toPandas()\n"," if pandas_df.empty:\n"," return\n"," \n"," if \"timestamp\" in pandas_df:\n"," # Filter out only for the latest window for the user_id\n"," pandas_df = pandas_df.sort_values(by=[\"user_id\",\"timestamp\"], ascending=False).groupby(\"user_id\").nth(-1)\n"," store.push(\"transactions_1d\", pandas_df)\n","\n","# 4) Launch streaming job to do all the above\n","query = (\n"," stream_agg\n"," .writeStream\n"," .outputMode(\"append\") \n"," .option(\"checkpointLocation\", \"/tmp/feast-workshop/\")\n"," .trigger(processingTime=\"30 seconds\")\n"," .foreachBatch(send_to_feast)\n"," .start()\n",")\n","\n","# 5) (not shown) Orchestrate this job so it's self-healing. Monitor to ensure features land in Feast.\n","query.awaitTermination(timeout=30)\n","query.stop()"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"a52ecca0-7135-44fd-80dd-1acf6bbc2503","showTitle":false,"title":""}},"source":["## Retrieve features"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"ae401586-7e84-4597-b736-e2764294fd24","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["
transaction_iduser_idis_fraudevent_timestampamt_sum_1d_10mamt_mean_1d_10m
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Showing the first 1000 rows.
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feast import FeatureStore\n","store = FeatureStore(config=repo_config)\n","\n","# To train a model, you need labeled events, and point in time correct features. Feast provides the features.\n","# Labeled events: historical log of user transactions (+ is_fraud)\n","# 1) Pull in user transaction features in that you need to train your model\n","entity_sql = f\"\"\"\n"," SELECT\n"," transaction_id,\n"," user_id,\n"," is_fraud, \n"," to_timestamp(timestamp) as event_timestamp\n"," FROM {store.get_data_source(\"transactions_source\").get_table_query_string()}\n"," WHERE \n"," timestamp BETWEEN '2022-04-20' AND '2022-04-21' AND\n"," partition_0 = \"2022\" AND\n"," partition_1 = \"04\" AND\n"," partition_2 = \"20\"\n","\"\"\"\n","training_df = store.get_historical_features(\n"," entity_df=entity_sql,\n"," features=store.get_feature_service(\"model_v1\"),\n",").to_spark_df()\n","# model.fit(training_df)\n","display(training_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"1598dbdb-532a-496f-885e-56555d429002","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
amt_mean_1d_10m : [58.9284504132231]\n","amt_sum_1d_10m : [28521.36999999998]\n","user_id : ['user_268308151877']\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
amt_mean_1d_10m : [58.9284504132231]\namt_sum_1d_10m : [28521.36999999998]\nuser_id : ['user_268308151877']\n
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["# 2) For an incoming transaction, pull the latest feature values for the associated user\n","# Note: Feast decoupled, so caller doesn't think about whether to pull this from a batch or streaming source\n","feature_vector = store.get_online_features(\n"," features=store.get_feature_service(\"model_v1\"),\n"," entity_rows=[\n"," {\n"," \"user_id\": \"user_268308151877\",\n"," }\n"," ],\n",").to_dict()\n","# model.predict(feature_vector)\n","\n","def print_online_features(feature_vector):\n"," for key, value in sorted(features.items()):\n"," print(key, \" : \", value)\n","\n","print_online_features(feature_vector)"]}],"metadata":{"application/vnd.databricks.v1+notebook":{"dashboards":[],"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":402528431658022,"dataframes":["_sqldf"]},"pythonIndentUnit":2},"notebookName":"Feast demo","notebookOrigID":1254642919516165,"widgets":{}},"kernelspec":{"display_name":"Python 3.8.10 64-bit ('python-3.8')","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.8.10"},"vscode":{"interpreter":{"hash":"7d634b9af180bcb32a446a43848522733ff8f5bbf0cc46dba1a83bede04bf237"}}},"nbformat":4,"nbformat_minor":0} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "0c4646a7-272d-44ec-95fe-3480a267e173", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "%pip install feast[spark,aws,redis]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "d47268f0-01e8-442c-be33-97b11f444aaf", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Try fetching data from a data source" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "8a29f106-0008-462c-876e-1dfb13ecff08", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from feast import FeatureStore, RepoConfig\n", + "from feast.repo_config import RegistryConfig\n", + "from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig\n", + "from feast.infra.offline_stores.contrib.spark_offline_store.spark import SparkOfflineStoreConfig\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n", + "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) \n", + "\n", + "repo_config = RepoConfig(\n", + " registry=RegistryConfig(\n", + " registry_type=\"sql\", \n", + " path=\"postgresql://postgres:mysecretpassword@[YOUR-RDS-ENDPOINT:PORT]:5432/feast\"\n", + " ),\n", + " project=\"feast_demo\",\n", + " provider=\"local\",\n", + " offline_store=SparkOfflineStoreConfig(\n", + " spark_conf={\n", + " \"spark.ui.enabled\": \"false\",\n", + " \"spark.eventLog.enabled\": \"false\",\n", + " \"spark.sql.catalogImplementation\": \"hive\",\n", + " \"spark.sql.parser.quotedRegexColumnNames\": \"true\",\n", + " \"spark.sql.session.timeZone\": \"UTC\"\n", + " }\n", + " ),\n", + " online_store=DynamoDBOnlineStoreConfig(region=\"us-west-1\"),\n", + " entity_key_serialization_version=2\n", + ")\n", + "store = FeatureStore(config=repo_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "662c84bf-b655-4da2-8f59-4bf82b92241c", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Managing data pipelines\n", + "\n", + "### Part 1: Moving the latest feature values into a low latency store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "83ad34fa-cbd3-4100-8c01-40720ddbc14e", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n", + "\n", + "user_transaction_amount_metrics:\n", + "Pulling latest features from spark offline store\n", + "\r", + " 0%| | 0/46 [00:00<?, ?it/s]\r", + " 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r", + "100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n\nuser_transaction_amount_metrics:\nPulling latest features from spark offline store\n\r 0%| | 0/46 [00:00<?, ?it/s]\r 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from datetime import datetime\n", + "store.materialize(\n", + " start_date=datetime(2022,4,20), \n", + " end_date=datetime(2022,4,21), \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "1921ca3d-011a-4171-9d7b-9c55e5464f01", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Part 2: Computing + pushing streaming feature values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "b45f5951-bf01-48ae-8b76-3db0390508a8", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from datetime import timedelta\n", + "from pyspark.sql.functions import col, from_json, from_utc_timestamp, when\n", + "from pyspark.sql.types import StructType, StructField, StringType, DoubleType, TimestampType, BooleanType, IntegerType\n", + "\n", + "def raw_data_deserialization(df):\n", + " payload_schema = StructType([\n", + " StructField('user_id', StringType(), False),\n", + " StructField('transaction_id', StringType(), False),\n", + " StructField('category', StringType(), False),\n", + " StructField('amt', StringType(), False),\n", + " StructField('is_fraud', StringType(), False),\n", + " StructField('merchant', StringType(), False),\n", + " StructField('merch_lat', StringType(), False),\n", + " StructField('merch_long', StringType(), False),\n", + " StructField('timestamp', StringType(), False),\n", + " ])\n", + "\n", + " return (\n", + " df.selectExpr('cast (data as STRING) jsonData')\n", + " .select(from_json('jsonData', payload_schema).alias('payload'))\n", + " .select(\n", + " col('payload.user_id').alias('user_id'),\n", + " col('payload.transaction_id').alias('transaction_id'),\n", + " col('payload.category').alias('category'),\n", + " col('payload.amt').cast('double').alias('amt'),\n", + " col('payload.is_fraud').cast('long').alias('is_fraud'),\n", + " col('payload.merchant').alias('merchant'),\n", + " col('payload.merch_lat').cast('double').alias('merch_lat'),\n", + " col('payload.merch_long').cast('double').alias('merch_long'),\n", + " from_utc_timestamp('payload.timestamp', 'UTC').alias('timestamp')\n", + " )\n", + " )\n", + "\n", + "def ingest_and_deserialize_kinesis_df():\n", + " options = {\n", + " \"streamName\": \"[REDACTED STREAM NAME]\",\n", + " \"roleArn\": \"[REDACTED ROLE]\",\n", + " \"region\": \"us-west-2\",\n", + " \"shardFetchInterval\": \"30s\",\n", + " \"initialPosition\": \"latest\"\n", + " }\n", + " reader = spark.readStream.format(\"kinesis\").options(**options)\n", + " df = reader.load()\n", + " df = raw_data_deserialization(df)\n", + " watermark = \"{} seconds\".format(timedelta(hours=24).seconds)\n", + " df = df.withWatermark(\"timestamp\", watermark)\n", + " return df\n", + "\n", + "# 1) Ingest and deserialize streaming events from Kinesis stream\n", + "df = ingest_and_deserialize_kinesis_df()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "30c5835d-64fd-4439-b99c-a3c58ac7673a", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.sql.functions import *\n", + "import pandas as pd\n", + "\n", + "# 2) Aggregate feature values\n", + "stream_agg = (\n", + " df.withWatermark(\"timestamp\", \"1 second\") \n", + " .groupBy(\"user_id\", window(timeColumn=\"timestamp\", windowDuration=\"1 day\", slideDuration=\"10 minutes\"))\n", + " .agg(\n", + " sum(\"amt\").alias(\"amt_sum_1d_10m\"),\n", + " avg(\"amt\").alias(\"amt_mean_1d_10m\")\n", + " )\n", + " .select(\"user_id\", col(\"window.end\").alias(\"timestamp\"), \"amt_sum_1d_10m\", \"amt_mean_1d_10m\")\n", + ")\n", + "\n", + "# 3) Push transformed features into Feast\n", + "from feast import FeatureStore\n", + "store = FeatureStore(config=repo_config)\n", + "\n", + "def send_to_feast(df, epoch):\n", + " pandas_df: pd.DataFrame = df.toPandas()\n", + " if pandas_df.empty:\n", + " return\n", + " \n", + " if \"timestamp\" in pandas_df:\n", + " # Filter out only for the latest window for the user_id\n", + " pandas_df = pandas_df.sort_values(by=[\"user_id\",\"timestamp\"], ascending=False).groupby(\"user_id\").nth(-1)\n", + " store.push(\"transactions_1d\", pandas_df)\n", + "\n", + "# 4) Launch streaming job to do all the above\n", + "query = (\n", + " stream_agg\n", + " .writeStream\n", + " .outputMode(\"append\") \n", + " .option(\"checkpointLocation\", \"/tmp/feast-workshop/\")\n", + " .trigger(processingTime=\"30 seconds\")\n", + " .foreachBatch(send_to_feast)\n", + " .start()\n", + ")\n", + "\n", + "# 5) (not shown) Orchestrate this job so it's self-healing. Monitor to ensure features land in Feast.\n", + "query.awaitTermination(timeout=30)\n", + "query.stop()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "a52ecca0-7135-44fd-80dd-1acf6bbc2503", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Retrieve features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "ae401586-7e84-4597-b736-e2764294fd24", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "plotOptions": { + "customPlotOptions": {}, + "displayType": "table", + "pivotAggregation": null, + "pivotColumns": null, + "xColumns": null, + "yColumns": null + }, + "removedWidgets": [], + "schema": [ + { + "metadata": "{}", + "name": "transaction_id", + "type": "\"string\"" + }, + { + "metadata": "{}", + "name": "user_id", + "type": "\"string\"" + }, + { + "metadata": "{}", + "name": "is_fraud", + "type": "\"long\"" + }, + { + "metadata": "{}", + "name": "event_timestamp", + "type": "\"timestamp\"" + }, + { + "metadata": "{}", + "name": "amt_sum_1d_10m", + "type": "\"double\"" + }, + { + "metadata": "{}", + "name": "amt_mean_1d_10m", + "type": "\"double\"" + } + ], + "type": "table" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from feast import FeatureStore\n", + "store = FeatureStore(config=repo_config)\n", + "\n", + "# To train a model, you need labeled events, and point in time correct features. Feast provides the features.\n", + "# Labeled events: historical log of user transactions (+ is_fraud)\n", + "# 1) Pull in user transaction features in that you need to train your model\n", + "entity_sql = f\"\"\"\n", + " SELECT\n", + " transaction_id,\n", + " user_id,\n", + " is_fraud, \n", + " to_timestamp(timestamp) as event_timestamp\n", + " FROM {store.get_data_source(\"transactions_source\").get_table_query_string()}\n", + " WHERE \n", + " timestamp BETWEEN '2022-04-20' AND '2022-04-21' AND\n", + " partition_0 = \"2022\" AND\n", + " partition_1 = \"04\" AND\n", + " partition_2 = \"20\"\n", + "\"\"\"\n", + "training_df = store.get_historical_features(\n", + " entity_df=entity_sql,\n", + " features=store.get_feature_service(\"model_v1\"),\n", + ").to_spark_df()\n", + "# model.fit(training_df)\n", + "display(training_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "1598dbdb-532a-496f-885e-56555d429002", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
amt_mean_1d_10m : [58.9284504132231]\n", + "amt_sum_1d_10m : [28521.36999999998]\n", + "user_id : ['user_268308151877']\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
amt_mean_1d_10m : [58.9284504132231]\namt_sum_1d_10m : [28521.36999999998]\nuser_id : ['user_268308151877']\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# 2) For an incoming transaction, pull the latest feature values for the associated user\n", + "# Note: Feast decoupled, so caller doesn't think about whether to pull this from a batch or streaming source\n", + "feature_vector = store.get_online_features(\n", + " features=store.get_feature_service(\"model_v1\"),\n", + " entity_rows=[\n", + " {\n", + " \"user_id\": \"user_268308151877\",\n", + " }\n", + " ],\n", + ").to_dict()\n", + "# model.predict(feature_vector)\n", + "\n", + "def print_online_features(feature_vector):\n", + " for key, value in sorted(features.items()):\n", + " print(key, \" : \", value)\n", + "\n", + "print_online_features(feature_vector)" + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "language": "python", + "notebookMetadata": { + "mostRecentlyExecutedCommandWithImplicitDF": { + "commandId": 402528431658022, + "dataframes": [ + "_sqldf" + ] + }, + "pythonIndentUnit": 2 + }, + "notebookName": "Feast demo", + "notebookOrigID": 1254642919516165, + "widgets": {} + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + }, + "vscode": { + "interpreter": { + "hash": "7d634b9af180bcb32a446a43848522733ff8f5bbf0cc46dba1a83bede04bf237" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module_5_compute_engine/README.md b/module_5_compute_engine/README.md new file mode 100644 index 0000000..750717d --- /dev/null +++ b/module_5_compute_engine/README.md @@ -0,0 +1,312 @@ +

Module 3: Orchestrated batch / stream transformations using dbt + Airflow with Feast (Databricks)

+ +> **Note:** This module is still WIP, and does not have a public data set to use. There is a smaller dataset visible in `data/` + +This is a very similar module to module 1. The key difference is now we'll be using Databricks in combination with dbt + Airflow to ensure that batch features are regularly generated. + +**Caveats** +- Feast does not itself handle orchestration of data pipelines (transforming features, materialization) and relies on the user to configure this with tools like dbt and Airflow. +- Feast does not ensure consistency in transformation logic between batch and stream features + +**Architecture** +- **Data sources**: Hive + Kinesis +- **Compute**: Databricks +- **Online store**: DynamoDB +- **Orchestrator**: Airflow + dbt +- **Use case**: Fraud detection + + + +

Table of Contents

+ +- [Workshop](#workshop) + - [Step 1: Setup](#step-1-setup) + - [Step 1a: Install Feast](#step-1a-install-feast) + - [Step 1b: Spin up Postgres database and Databricks](#step-1b-spin-up-postgres-database-and-databricks) + - [Step 1c: Set up dbt models for batch transformations](#step-1c-set-up-dbt-models-for-batch-transformations) + - [Step 2: Configure Feast](#step-2-configure-feast) + - [Step 2a: Inspect the `feature_repo` directory for the feature definitions.](#step-2a-inspect-the-feature_repo-directory-for-the-feature-definitions) + - [Step 2b: Inspect the `feature_store.yaml`](#step-2b-inspect-the-feature_storeyaml) + - [Step 3: Run `feast apply`](#step-3-run-feast-apply) + - [Step 4: Ingest features](#step-4-ingest-features) + - [Step 4a: Streaming features](#step-4a-streaming-features) + - [Step 4b: Backfilling batch features on a schedule (dbt + Airflow)](#step-4b-backfilling-batch-features-on-a-schedule-dbt--airflow) + - [Examine the Airflow DAG](#examine-the-airflow-dag) + - [Setup Airflow to talk to dbt](#setup-airflow-to-talk-to-dbt) + - [Enable the Airflow DAG and see the UI](#enable-the-airflow-dag-and-see-the-ui) + - [Q: What if different feature views have different freshness requirements?](#q-what-if-different-feature-views-have-different-freshness-requirements) + - [(optional): Run a backfill](#optional-run-a-backfill) + - [Step 5: Retrieve features](#step-5-retrieve-features) + - [Overview](#overview) + - [Time to run code!](#time-to-run-code) + - [Demo: Generating training data](#demo-generating-training-data) + - [Demo: Fetching online features to predict fraud](#demo-fetching-online-features-to-predict-fraud) + - [Step 6: Options for orchestrating streaming pipelines](#step-6-options-for-orchestrating-streaming-pipelines) +- [Conclusion](#conclusion) + - [Limitations](#limitations) + - [Why Feast?](#why-feast) +- [FAQ](#faq) + - [How do I iterate on features?](#how-do-i-iterate-on-features) + - [How does this work in production?](#how-does-this-work-in-production) + +# Workshop +## Step 1: Setup +### Step 1a: Install Feast + +First, we install Feast with Spark, Postgres, and DynamoDB support: +```bash +pip install "feast[spark,postgres,aws]" +``` + +### Step 1b: Spin up Postgres database and Databricks +You'll need to spin up a PostgreSQL instance (e.g. AWS RDS) for the registry, and then replace the path in the above `feature_store.yaml`. + +You'll also need `databricks-connect` installed and configured so Feast can talk to Databricks. + +### Step 1c: Set up dbt models for batch transformations +> **TODO(adchia):** Generate parquet file to upload for public dataset for features + +There's already a dbt model in this repository that transforms batch data. You just need to change the [S3 staging location](module_3_db/dbt/feast_demo/dbt_project.yml) and instantiate it: + +> **Note:** You'll need to install dbt-databricks as well! `pip install dbt-databricks` + +To initialize dbt with your own credentials, do this +```bash +# After updating the dbt_project.yml with your S3 location +cd dbt/feast_demo; dbt init; dbt run +``` + +This will create the initial tables we need for Feast: +![](demo_images/dbt.png) + +## Step 2: Configure Feast +### Step 2a: Inspect the `feature_repo` directory for the feature definitions. +This includes the data sources, feature schemas (feature views), and model versions (feature services). + +### Step 2b: Inspect the `feature_store.yaml` +This configures how Feast communicates with services you manage to make features available for training / serving. + +```yaml +project: feast_demo +provider: aws +registry: + registry_type: sql + path: postgresql://postgres:mysecretpassword@[your-rds-instance]:5432/feast +online_store: + type: dynamodb + region: us-west-1 +offline_store: + type: spark + spark_conf: # Note: pip install -U "databricks-connect" + spark.ui.enabled: "false" + spark.eventLog.enabled: "false" + spark.sql.catalogImplementation: "hive" + spark.sql.parser.quotedRegexColumnNames: "true" + spark.sql.session.timeZone: "UTC" +entity_key_serialization_version: 2 +``` +## Step 3: Run `feast apply` +In this example, we're using a test data source registered in Hive Metastore / AWS Glue Catalog (backed by an S3 bucket). + +To get started, go ahead and register the feature repository +```console +$ cd feature_repo; feast apply + +Created entity user +Created feature view user_transaction_amount_metrics +Created feature service model_v1 + +Deploying infrastructure for user_transaction_amount_metrics +``` + +## Step 4: Ingest features +As seen in below, to compute a daily transaction aggregate feature, we need to: +1. Generate streaming features + - (outside of Feast): Connect to stream, transform features + - (Feast): Push above feature values into Feast +2. Backfill feature values from historical stream logs + - (outside of Feast with dbt + Airflow): Transform raw data into batch features + - (Feast + Airflow): Backfill (materialize) feature values into the online store. + + + +### Step 4a: Streaming features +To achieve fresher features, one might consider using streaming compute.There are two broad approaches with streaming +1. **[Simple, semi-fresh features]** Use data warehouse / data lake specific streaming ingest of raw data. + - This means that Feast only needs to know about a "batch feature" because the assumption is those batch features are sufficiently fresh. + - **BUT** there are limits to how fresh your features are. You won't be able to get to minute level freshness. +2. **[Complex, very fresh features]** Build separate streaming pipelines for very fresh features + - It is on you to build out a separate streaming pipeline (e.g. using Spark Structured Streaming or Flink), ensuring the transformation logic is consistent with batch transformations, and calling the push API as per [module 1](../module_1/README.md). + +Feast will help enforce a consistent schema across batch + streaming features as they land in the online store. + +See the sample notebook you can import into Databricks [here](sample_db_notebook.ipynb) to see how you ingest from a stream, transform it, and finally push the transformed features into Feast. + +![](demo_images/db_stream_agg.png) + +> **Note:** Not shown here is the need to orchestrate + monitor this job (for each feature view) and ensure it continues to land in Feast. You'll have to set that up outside Feast. + +### Step 4b: Backfilling batch features on a schedule (dbt + Airflow) +- You'll want to first run a backfill job (to populate the online store from all historical data). +- Then, you want to orchestrate more daily jobs that loads new incoming batch features into the online store. + +#### Examine the Airflow DAG +The example dag is going to run on a daily basis and materialize *all* feature views based on the start and end interval. Note that there is a 1 hr overlap in the start time to account for potential late arriving data in the offline store. + +With dbt incremental models, the model itself in incremental mode selects overlapping windows of data to account for late arriving data. Feast materialization similarly has a late arriving threshold. + +```python +with DAG( + dag_id="feature_dag", + start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), + description="A dbt + Feast DAG", + schedule="@daily", + catchup=False, + tags=["feast"], +) as dag: + dbt_test = BashOperator( + task_id="dbt_test", + bash_command=""" + cd ${AIRFLOW_HOME}; dbt test --models "aggregate_transaction_features" + """, + dag=dag, + ) + + # Generate new transformed feature values + dbt_run = BashOperator( + task_id="dbt_run", + bash_command=""" + cd ${AIRFLOW_HOME}; dbt run --models "aggregate_transaction_features" + """, + dag=dag, + ) + + # Use Feast to make these feature values available in a low latency store + @task() + def materialize(data_interval_start=None, data_interval_end=None): + repo_config = RepoConfig( + registry=RegistryConfig( + registry_type="sql", + path="postgresql://postgres:mysecretpassword@[YOUR-RDS-ENDPOINT:PORT]/feast", + ), + project="feast_demo", + provider="local", + offline_store=SparkOfflineStoreConfig( + spark_conf={ + "spark.ui.enabled": "false", + "spark.eventLog.enabled": "false", + "spark.sql.catalogImplementation": "hive", + "spark.sql.parser.quotedRegexColumnNames": "true", + "spark.sql.session.timeZone": "UTC", + } + ), + online_store=DynamoDBOnlineStoreConfig(region="us-west-1"), + entity_key_serialization_version=2, + ) + store = FeatureStore(config=repo_config) + # Add 1 hr overlap to account for late data + # Note: normally, you'll probably have different feature views with different freshness requirements, instead + # of materializing all feature views every day. + store.materialize(data_interval_start.subtract(hours=1), data_interval_end) + + # Setup DAG + dbt_test >> dbt_run >> materialize() +``` +#### Setup Airflow to talk to dbt +We setup a standalone version of Airflow to set up the `PythonOperator` (Airflow now prefers @task for this) and `BashOperator` which will run incremental dbt models. We use dbt to transform raw data on Databricks, and once the incremental model is tested / ran, we run materialization. + +The below script will copy the dbt DAGs over. In production, you'd want to use Airflow to sync with version controlled dbt DAGS (e.g. that are sync'd to S3). + +```bash +# FIRST: Update the DAG to reference your RDS registry +# SECOND: Update setup_airflow.sh to use your Databricks credentials +cd ../airflow_demo; sh setup_airflow.sh +``` + +#### Enable the Airflow DAG and see the UI +Now go to `localhost:8080`, use Airflow's auto-generated admin password to login, and toggle on the DAG. It should run one task automatically. After waiting for a run to finish, you'll see a successful job: + +![](demo_images/airflow.png) + +#### Q: What if different feature views have different freshness requirements? + +There's no built in mechanism for this, but you could store this logic in the feature view tags (e.g. a `batch_schedule`). + +Then, you can parse these feature view in your Airflow job. You could for example have one DAG that runs all the daily `batch_schedule` feature views, and another DAG that runs all feature views with an hourly `batch_schedule`. + +#### (optional): Run a backfill +To run a backfill (i.e. process previous days of the above while letting Airflow manage state), you can do (from the `airflow_demo` directory): + +> **Warning:** This works correctly with the Redis online store because it conditionally writes. This logic has not been implemented for other online stores yet, and so can result in incorrect behavior + +```bash +export AIRFLOW_HOME=$(pwd)/airflow_home +airflow dags backfill \ + --start-date 2021-07-01 \ + --end-date 2021-07-15 \ + feature_dag +``` + +## Step 5: Retrieve features +### Overview +Feast exposes a `get_historical_features` method to generate training data / run batch scoring and `get_online_features` method to power model serving. + +### Time to run code! +See the sample notebook you can import into Databricks [here](sample_db_notebook.ipynb) + +#### Demo: Generating training data +![](demo_images/db_ghf.png) + +#### Demo: Fetching online features to predict fraud +![](demo_images/db_gof.png) + +## Step 6: Options for orchestrating streaming pipelines +We don't showcase how this works, but broadly there are many approaches to this. In all the approaches, you'll likely want to generate operational metrics for monitoring (e.g. via StatsD or Prometheus Pushgateway). + +To outline a few approaches: + - **Option 1**: frequently run stream ingestion on a trigger, and then run this in the orchestration tool of choice like Airflow, Databricks Jobs, etc. e.g. + ```python + (stream_agg + .writeStream + .outputMode("append") + .option("checkpointLocation", "/tmp/feast-workshop/q3/") + .trigger(once=True) + .foreachBatch(send_to_feast) + .start()) + ``` + - **Option 2**: with Databricks, use Databricks Jobs to monitor streaming queries and auto-retry on a new cluster + on failure. See [Databricks docs](https://docs.databricks.com/structured-streaming/query-recovery.html#configure-structured-streaming-jobs-to-restart-streaming-queries-on-failure) for details. + - **Option 3**: with Dataproc, configure [restartable jobs](https://cloud.google.com/dataproc/docs/concepts/jobs/restartable-jobs) + - **Option 4** If you're using Flink, then consider configuring a [restart strategy](https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/ops/state/task_failure_recovery/) + +# Conclusion +By the end of this module, you will have learned how to build a full feature platform, with orchestrated batch transformations (using dbt + Airflow), orchestrated materialization (with Feast + Airflow), and pointers on orchestrating streaming transformations. + +## Limitations +- Feast does not itself handle orchestration of transformation or materialization, and relies on the user to configure this with tools like dbt and Airflow. +- Feast does not ensure consistency in transformation logic between batch and stream features + +## Why Feast? +Feast abstracts away the need to think about data modeling in the online store and helps you: +- maintain fresh features in the online store by + - ingesting batch features into the online store (via `feast materialize` or `feast materialize-incremental`) + - ingesting streaming features into the online store (e.g. through `feature_store.push` or a Push server endpoint (`/push`)) +- serve features (e.g. through `feature_store.get_online_features` or through feature servers) + +# FAQ + +### How do I iterate on features? +Once a feature view is in production, best practice is to create a new feature view (+ a separate dbt model) to generate new features or change existing features, so-as not to negatively impact prediction quality. + +This means for each new set of features, you'll need to: +1. Define a new dbt model +2. Define a new Feast data source + feature view, and feature service (model version) that depends on these features. +3. Ensure the transformations + materialization jobs are executed at the right cadence with Airflow + dbt + Feast. + +### How does this work in production? +Several things change: +- All credentials are secured as secrets +- dbt models are version controlled +- Production deployment of Airflow (e.g. syncing with a Git repository of DAGs, using k8s) +- Bundling dbt models with Airflow (e.g. via S3 like this [MWAA + dbt guide](https://docs.aws.amazon.com/mwaa/latest/userguide/samples-dbt.html)) +- Airflow DAG parallelizes across feature views (instead of running a single `feature_store.materialize` across all feature views) +- Feast materialization is configured to be more scalable (e.g. using other Feast batch materialization engines [Bytewax](https://docs.feast.dev/reference/batch-materialization/bytewax), [Snowflake](https://docs.feast.dev/reference/batch-materialization/snowflake), [Lambda](https://docs.feast.dev/reference/batch-materialization/lambda), 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time.time()\n", + "elapsed_time = end_time - start_time\n", + "print(f\"Elapsed time: {elapsed_time} seconds\")" + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "language": "python", + "notebookMetadata": { + "mostRecentlyExecutedCommandWithImplicitDF": { + "commandId": 402528431658022, + "dataframes": [ + "_sqldf" + ] + }, + "pythonIndentUnit": 2 + }, + "notebookName": "Feast demo", + "notebookOrigID": 1254642919516165, + "widgets": {} + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + }, + "vscode": { + "interpreter": { + "hash": "7d634b9af180bcb32a446a43848522733ff8f5bbf0cc46dba1a83bede04bf237" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module_5_compute_engine/feature_repo/data_sources.py b/module_5_compute_engine/feature_repo/data_sources.py new file mode 100644 index 0000000..00a4184 --- /dev/null +++ b/module_5_compute_engine/feature_repo/data_sources.py @@ -0,0 +1,25 @@ +from feast import ( + Field, + FileSource, + PushSource, + RequestSource, + SnowflakeSource +) +from feast.types import Int64, Float32 + +# driver_stats = SparkSource( +# name="driver_stats_source", +# path="../data/driver_stats_lat_lon.parquet", +# timestamp_field="event_timestamp", +# created_timestamp_column="created", +# description="A table describing the stats of a driver based on hourly logs", +# owner="test2@gmail.com", +# ) + + +tpch_sf = SnowflakeSource( + database="SNOWFLAKE_SAMPLE_DATA", + schema="TPCH_SF10", + table="ORDERS", + timestamp_field="O_ORDERDATE" +) \ No newline at end of file diff --git a/module_5_compute_engine/feature_repo/entities.py b/module_5_compute_engine/feature_repo/entities.py new file mode 100644 index 0000000..8bd44b4 --- /dev/null +++ b/module_5_compute_engine/feature_repo/entities.py @@ -0,0 +1,11 @@ +from feast import ( + Entity, + ValueType, +) + +customer = Entity( + name="customer", + join_keys=["O_CUSTKEY"], + value_type=ValueType.INT64, + description="Custoemr id", +) diff --git a/module_5_compute_engine/feature_repo/feature_services.py b/module_5_compute_engine/feature_repo/feature_services.py new file mode 100644 index 0000000..3b442ec --- /dev/null +++ b/module_5_compute_engine/feature_repo/feature_services.py @@ -0,0 +1,9 @@ +from feast import FeatureService + +from features import * +# +# feature_service = FeatureService( +# name="model_v1", +# features=[bfv[["conv_rate"]]], +# owner="test3@gmail.com", +# ) diff --git a/module_5_compute_engine/feature_repo/feature_store.yaml b/module_5_compute_engine/feature_repo/feature_store.yaml new file mode 100644 index 0000000..2b076cd --- /dev/null +++ b/module_5_compute_engine/feature_repo/feature_store.yaml @@ -0,0 +1,33 @@ +project: feast_demo_compute_engine +provider: local +registry: + registry_type: sql + path: postgresql+psycopg://@localhost:5432/feast +online_store: + type: postgres + host: 127.0.0.1 + port: 5432 + database: feast + user: "" + password: "" +offline_store: + type: snowflake.offline + account: ${SNOWFLAKE_ACCOUNT} + user: ${SNOWFLAKE_USER} + password: ${SNOWFLAKE_PASSWORD} + role: PUBLIC + warehouse: COMPUTE_WH + database: SNOWFLAKE_SAMPLE_DATA + schema: TPCH_SF10 +batch_engine: + type: "spark.engine" + spark_conf: + spark.master: "local[*]" + spark.ui.enabled: "false" + spark.eventLog.enabled: "false" + spark.sql.catalogImplementation: "hive" + spark.sql.parser.quotedRegexColumnNames: "true" + spark.sql.session.timeZone: "UTC" + spark.sql.execution.arrow.fallback.enabled: "true" + spark.sql.execution.arrow.pyspark.enabled: "true" + partitions: 2 \ No newline at end of file diff --git a/module_5_compute_engine/feature_repo/features.py b/module_5_compute_engine/feature_repo/features.py new file mode 100644 index 0000000..18be34b --- /dev/null +++ b/module_5_compute_engine/feature_repo/features.py @@ -0,0 +1,30 @@ +from datetime import timedelta + +from feast import ( + BatchFeatureView, +) +import pyspark +from feast.types import String + +from data_sources import * +from entities import * + + +def transform_feature(inputs: pyspark.sql.DataFrame): + return inputs + + +bfv = BatchFeatureView( + name="order_stats", + description="Hourly features", + entities=[customer], + schema=[ + Field(name="O_TOTALPRICE", dtype=Float32), + Field(name="O_ORDERSTATUS", dtype=String), + ], + udf=transform_feature, + online=True, + source=tpch_sf, + tags={"production": "True"}, + owner="test2@gmail.com", +) diff --git a/module_5_compute_engine/orchestrate.png b/module_5_compute_engine/orchestrate.png new file mode 100644 index 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+authors = ["Hao Xu"] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.10" +# change to use feast after release 0.50.0 +feast = {path = "../../feast", develop = true} +pandas = "2.2.3" +numpy = "1.26.4" +setuptools = "^80.9.0" +pyspark = "^4.0.0" +jupyter = "^1.1.1" +psycopg = "^3.2.9" +psycopg-pool = "^3.2.6" +cryptography = "^45.0.4" +snowflake-connector-python = "^3.15.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" From f2beb59dd276957bc50876b255ce563387aac11b Mon Sep 17 00:00:00 2001 From: Hao Xu Date: Tue, 10 Jun 2025 21:37:07 -0700 Subject: [PATCH 2/4] add compute example Signed-off-by: Hao Xu --- module_5_compute_engine/README.md | 315 +---------------------- module_5_compute_engine/architecture.png | Bin 167522 -> 0 bytes module_5_compute_engine/orchestrate.png | Bin 222627 -> 0 bytes 3 files changed, 4 insertions(+), 311 deletions(-) delete mode 100644 module_5_compute_engine/architecture.png delete mode 100644 module_5_compute_engine/orchestrate.png diff --git a/module_5_compute_engine/README.md b/module_5_compute_engine/README.md index 750717d..e39b4c9 100644 --- a/module_5_compute_engine/README.md +++ b/module_5_compute_engine/README.md @@ -1,312 +1,5 @@ -

Module 3: Orchestrated batch / stream transformations using dbt + Airflow with Feast (Databricks)

+# Feast Compute Engine Demo +This is a demo of the Feast Compute Engine, which allows you to run feature transformations(filter, aggregation, custom udf etc.) by specified compute engine. -> **Note:** This module is still WIP, and does not have a public data set to use. There is a smaller dataset visible in `data/` - -This is a very similar module to module 1. The key difference is now we'll be using Databricks in combination with dbt + Airflow to ensure that batch features are regularly generated. - -**Caveats** -- Feast does not itself handle orchestration of data pipelines (transforming features, materialization) and relies on the user to configure this with tools like dbt and Airflow. -- Feast does not ensure consistency in transformation logic between batch and stream features - -**Architecture** -- **Data sources**: Hive + Kinesis -- **Compute**: Databricks -- **Online store**: DynamoDB -- **Orchestrator**: Airflow + dbt -- **Use case**: Fraud detection - - - -

Table of Contents

- -- [Workshop](#workshop) - - [Step 1: Setup](#step-1-setup) - - [Step 1a: Install Feast](#step-1a-install-feast) - - [Step 1b: Spin up Postgres database and Databricks](#step-1b-spin-up-postgres-database-and-databricks) - - [Step 1c: Set up dbt models for batch transformations](#step-1c-set-up-dbt-models-for-batch-transformations) - - [Step 2: Configure Feast](#step-2-configure-feast) - - [Step 2a: Inspect the `feature_repo` directory for the feature definitions.](#step-2a-inspect-the-feature_repo-directory-for-the-feature-definitions) - - [Step 2b: Inspect the `feature_store.yaml`](#step-2b-inspect-the-feature_storeyaml) - - [Step 3: Run `feast apply`](#step-3-run-feast-apply) - - [Step 4: Ingest features](#step-4-ingest-features) - - [Step 4a: Streaming features](#step-4a-streaming-features) - - [Step 4b: Backfilling batch features on a schedule (dbt + Airflow)](#step-4b-backfilling-batch-features-on-a-schedule-dbt--airflow) - - [Examine the Airflow DAG](#examine-the-airflow-dag) - - [Setup Airflow to talk to dbt](#setup-airflow-to-talk-to-dbt) - - [Enable the Airflow DAG and see the UI](#enable-the-airflow-dag-and-see-the-ui) - - [Q: What if different feature views have different freshness requirements?](#q-what-if-different-feature-views-have-different-freshness-requirements) - - [(optional): Run a backfill](#optional-run-a-backfill) - - [Step 5: Retrieve features](#step-5-retrieve-features) - - [Overview](#overview) - - [Time to run code!](#time-to-run-code) - - [Demo: Generating training data](#demo-generating-training-data) - - [Demo: Fetching online features to predict fraud](#demo-fetching-online-features-to-predict-fraud) - - [Step 6: Options for orchestrating streaming pipelines](#step-6-options-for-orchestrating-streaming-pipelines) -- [Conclusion](#conclusion) - - [Limitations](#limitations) - - [Why Feast?](#why-feast) -- [FAQ](#faq) - - [How do I iterate on features?](#how-do-i-iterate-on-features) - - [How does this work in production?](#how-does-this-work-in-production) - -# Workshop -## Step 1: Setup -### Step 1a: Install Feast - -First, we install Feast with Spark, Postgres, and DynamoDB support: -```bash -pip install "feast[spark,postgres,aws]" -``` - -### Step 1b: Spin up Postgres database and Databricks -You'll need to spin up a PostgreSQL instance (e.g. AWS RDS) for the registry, and then replace the path in the above `feature_store.yaml`. - -You'll also need `databricks-connect` installed and configured so Feast can talk to Databricks. - -### Step 1c: Set up dbt models for batch transformations -> **TODO(adchia):** Generate parquet file to upload for public dataset for features - -There's already a dbt model in this repository that transforms batch data. You just need to change the [S3 staging location](module_3_db/dbt/feast_demo/dbt_project.yml) and instantiate it: - -> **Note:** You'll need to install dbt-databricks as well! `pip install dbt-databricks` - -To initialize dbt with your own credentials, do this -```bash -# After updating the dbt_project.yml with your S3 location -cd dbt/feast_demo; dbt init; dbt run -``` - -This will create the initial tables we need for Feast: -![](demo_images/dbt.png) - -## Step 2: Configure Feast -### Step 2a: Inspect the `feature_repo` directory for the feature definitions. -This includes the data sources, feature schemas (feature views), and model versions (feature services). - -### Step 2b: Inspect the `feature_store.yaml` -This configures how Feast communicates with services you manage to make features available for training / serving. - -```yaml -project: feast_demo -provider: aws -registry: - registry_type: sql - path: postgresql://postgres:mysecretpassword@[your-rds-instance]:5432/feast -online_store: - type: dynamodb - region: us-west-1 -offline_store: - type: spark - spark_conf: # Note: pip install -U "databricks-connect" - spark.ui.enabled: "false" - spark.eventLog.enabled: "false" - spark.sql.catalogImplementation: "hive" - spark.sql.parser.quotedRegexColumnNames: "true" - spark.sql.session.timeZone: "UTC" -entity_key_serialization_version: 2 -``` -## Step 3: Run `feast apply` -In this example, we're using a test data source registered in Hive Metastore / AWS Glue Catalog (backed by an S3 bucket). - -To get started, go ahead and register the feature repository -```console -$ cd feature_repo; feast apply - -Created entity user -Created feature view user_transaction_amount_metrics -Created feature service model_v1 - -Deploying infrastructure for user_transaction_amount_metrics -``` - -## Step 4: Ingest features -As seen in below, to compute a daily transaction aggregate feature, we need to: -1. Generate streaming features - - (outside of Feast): Connect to stream, transform features - - (Feast): Push above feature values into Feast -2. Backfill feature values from historical stream logs - - (outside of Feast with dbt + Airflow): Transform raw data into batch features - - (Feast + Airflow): Backfill (materialize) feature values into the online store. - - - -### Step 4a: Streaming features -To achieve fresher features, one might consider using streaming compute.There are two broad approaches with streaming -1. **[Simple, semi-fresh features]** Use data warehouse / data lake specific streaming ingest of raw data. - - This means that Feast only needs to know about a "batch feature" because the assumption is those batch features are sufficiently fresh. - - **BUT** there are limits to how fresh your features are. You won't be able to get to minute level freshness. -2. **[Complex, very fresh features]** Build separate streaming pipelines for very fresh features - - It is on you to build out a separate streaming pipeline (e.g. using Spark Structured Streaming or Flink), ensuring the transformation logic is consistent with batch transformations, and calling the push API as per [module 1](../module_1/README.md). - -Feast will help enforce a consistent schema across batch + streaming features as they land in the online store. - -See the sample notebook you can import into Databricks [here](sample_db_notebook.ipynb) to see how you ingest from a stream, transform it, and finally push the transformed features into Feast. - -![](demo_images/db_stream_agg.png) - -> **Note:** Not shown here is the need to orchestrate + monitor this job (for each feature view) and ensure it continues to land in Feast. You'll have to set that up outside Feast. - -### Step 4b: Backfilling batch features on a schedule (dbt + Airflow) -- You'll want to first run a backfill job (to populate the online store from all historical data). -- Then, you want to orchestrate more daily jobs that loads new incoming batch features into the online store. - -#### Examine the Airflow DAG -The example dag is going to run on a daily basis and materialize *all* feature views based on the start and end interval. Note that there is a 1 hr overlap in the start time to account for potential late arriving data in the offline store. - -With dbt incremental models, the model itself in incremental mode selects overlapping windows of data to account for late arriving data. Feast materialization similarly has a late arriving threshold. - -```python -with DAG( - dag_id="feature_dag", - start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), - description="A dbt + Feast DAG", - schedule="@daily", - catchup=False, - tags=["feast"], -) as dag: - dbt_test = BashOperator( - task_id="dbt_test", - bash_command=""" - cd ${AIRFLOW_HOME}; dbt test --models "aggregate_transaction_features" - """, - dag=dag, - ) - - # Generate new transformed feature values - dbt_run = BashOperator( - task_id="dbt_run", - bash_command=""" - cd ${AIRFLOW_HOME}; dbt run --models "aggregate_transaction_features" - """, - dag=dag, - ) - - # Use Feast to make these feature values available in a low latency store - @task() - def materialize(data_interval_start=None, data_interval_end=None): - repo_config = RepoConfig( - registry=RegistryConfig( - registry_type="sql", - path="postgresql://postgres:mysecretpassword@[YOUR-RDS-ENDPOINT:PORT]/feast", - ), - project="feast_demo", - provider="local", - offline_store=SparkOfflineStoreConfig( - spark_conf={ - "spark.ui.enabled": "false", - "spark.eventLog.enabled": "false", - "spark.sql.catalogImplementation": "hive", - "spark.sql.parser.quotedRegexColumnNames": "true", - "spark.sql.session.timeZone": "UTC", - } - ), - online_store=DynamoDBOnlineStoreConfig(region="us-west-1"), - entity_key_serialization_version=2, - ) - store = FeatureStore(config=repo_config) - # Add 1 hr overlap to account for late data - # Note: normally, you'll probably have different feature views with different freshness requirements, instead - # of materializing all feature views every day. - store.materialize(data_interval_start.subtract(hours=1), data_interval_end) - - # Setup DAG - dbt_test >> dbt_run >> materialize() -``` -#### Setup Airflow to talk to dbt -We setup a standalone version of Airflow to set up the `PythonOperator` (Airflow now prefers @task for this) and `BashOperator` which will run incremental dbt models. We use dbt to transform raw data on Databricks, and once the incremental model is tested / ran, we run materialization. - -The below script will copy the dbt DAGs over. In production, you'd want to use Airflow to sync with version controlled dbt DAGS (e.g. that are sync'd to S3). - -```bash -# FIRST: Update the DAG to reference your RDS registry -# SECOND: Update setup_airflow.sh to use your Databricks credentials -cd ../airflow_demo; sh setup_airflow.sh -``` - -#### Enable the Airflow DAG and see the UI -Now go to `localhost:8080`, use Airflow's auto-generated admin password to login, and toggle on the DAG. It should run one task automatically. After waiting for a run to finish, you'll see a successful job: - -![](demo_images/airflow.png) - -#### Q: What if different feature views have different freshness requirements? - -There's no built in mechanism for this, but you could store this logic in the feature view tags (e.g. a `batch_schedule`). - -Then, you can parse these feature view in your Airflow job. You could for example have one DAG that runs all the daily `batch_schedule` feature views, and another DAG that runs all feature views with an hourly `batch_schedule`. - -#### (optional): Run a backfill -To run a backfill (i.e. process previous days of the above while letting Airflow manage state), you can do (from the `airflow_demo` directory): - -> **Warning:** This works correctly with the Redis online store because it conditionally writes. This logic has not been implemented for other online stores yet, and so can result in incorrect behavior - -```bash -export AIRFLOW_HOME=$(pwd)/airflow_home -airflow dags backfill \ - --start-date 2021-07-01 \ - --end-date 2021-07-15 \ - feature_dag -``` - -## Step 5: Retrieve features -### Overview -Feast exposes a `get_historical_features` method to generate training data / run batch scoring and `get_online_features` method to power model serving. - -### Time to run code! -See the sample notebook you can import into Databricks [here](sample_db_notebook.ipynb) - -#### Demo: Generating training data -![](demo_images/db_ghf.png) - -#### Demo: Fetching online features to predict fraud -![](demo_images/db_gof.png) - -## Step 6: Options for orchestrating streaming pipelines -We don't showcase how this works, but broadly there are many approaches to this. In all the approaches, you'll likely want to generate operational metrics for monitoring (e.g. via StatsD or Prometheus Pushgateway). - -To outline a few approaches: - - **Option 1**: frequently run stream ingestion on a trigger, and then run this in the orchestration tool of choice like Airflow, Databricks Jobs, etc. e.g. - ```python - (stream_agg - .writeStream - .outputMode("append") - .option("checkpointLocation", "/tmp/feast-workshop/q3/") - .trigger(once=True) - .foreachBatch(send_to_feast) - .start()) - ``` - - **Option 2**: with Databricks, use Databricks Jobs to monitor streaming queries and auto-retry on a new cluster + on failure. See [Databricks docs](https://docs.databricks.com/structured-streaming/query-recovery.html#configure-structured-streaming-jobs-to-restart-streaming-queries-on-failure) for details. - - **Option 3**: with Dataproc, configure [restartable jobs](https://cloud.google.com/dataproc/docs/concepts/jobs/restartable-jobs) - - **Option 4** If you're using Flink, then consider configuring a [restart strategy](https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/ops/state/task_failure_recovery/) - -# Conclusion -By the end of this module, you will have learned how to build a full feature platform, with orchestrated batch transformations (using dbt + Airflow), orchestrated materialization (with Feast + Airflow), and pointers on orchestrating streaming transformations. - -## Limitations -- Feast does not itself handle orchestration of transformation or materialization, and relies on the user to configure this with tools like dbt and Airflow. -- Feast does not ensure consistency in transformation logic between batch and stream features - -## Why Feast? -Feast abstracts away the need to think about data modeling in the online store and helps you: -- maintain fresh features in the online store by - - ingesting batch features into the online store (via `feast materialize` or `feast materialize-incremental`) - - ingesting streaming features into the online store (e.g. through `feature_store.push` or a Push server endpoint (`/push`)) -- serve features (e.g. through `feature_store.get_online_features` or through feature servers) - -# FAQ - -### How do I iterate on features? -Once a feature view is in production, best practice is to create a new feature view (+ a separate dbt model) to generate new features or change existing features, so-as not to negatively impact prediction quality. - -This means for each new set of features, you'll need to: -1. Define a new dbt model -2. Define a new Feast data source + feature view, and feature service (model version) that depends on these features. -3. Ensure the transformations + materialization jobs are executed at the right cadence with Airflow + dbt + Feast. - -### How does this work in production? -Several things change: -- All credentials are secured as secrets -- dbt models are version controlled -- Production deployment of Airflow (e.g. syncing with a Git repository of DAGs, using k8s) -- Bundling dbt models with Airflow (e.g. via S3 like this [MWAA + dbt guide](https://docs.aws.amazon.com/mwaa/latest/userguide/samples-dbt.html)) -- Airflow DAG parallelizes across feature views (instead of running a single `feature_store.materialize` across all feature views) -- Feast materialization is configured to be more scalable (e.g. using other Feast batch materialization engines [Bytewax](https://docs.feast.dev/reference/batch-materialization/bytewax), [Snowflake](https://docs.feast.dev/reference/batch-materialization/snowflake), [Lambda](https://docs.feast.dev/reference/batch-materialization/lambda), [Spark](https://docs.feast.dev/reference/batch-materialization/spark)) \ No newline at end of file +## Install and Run +Check notebook [compute_example.ipynb](compute_example.ipynb) for more details. diff --git a/module_5_compute_engine/architecture.png b/module_5_compute_engine/architecture.png deleted file mode 100644 index c6988d521b61ce46c1ee96159a2c292f9b501292..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 167522 zcmeFZWmr^g+dqm3C@6>`ASk7Tf`s%83?T;H-ALCELpLZOD1vl%clXdr58d54AT7NY zJ~ycEz4!A!_OU+91qEA3@Rc+Q3dSM|%5}|~ z7{HzG$;o&Wl-oRp0s@jk0s`ccmS%c}Cb}pnf<7S%Hxy-B31U?O1NaQwC6hl)qTO8) z#7yvHATjws$@laHhKDc7U3}GBw2w6f6kgv~y+v;PdY!=+&PZ@S*jkFPYMUC}YDA%i zV~WG}?6@o9JQ`%XILU>Q9y9&%6(}8yQqkaKA)3$oAx|J~{LTpO9zcU44{z2lVaFs8Ec4Nu>9%R14nC zYm(Z0>p@Xmem5*=n+ChzpcTkYL6A3MKGzziVjEj>5;ZFF5GqMw;m-S5au%I+S)FVK zMm-_g*22sZPUWP01J(~O8yo3sNLECj<x%nBXCF5ySX4-`dYqJPda*zg zF%82=f8vjai&1c`iN7Jb#T-2@l;P$(f!hi3|U*0Pc&ZeAH*C~2-5^NPe@MJ80 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1803a700eb411cb815597780aa029c9990779c0f Mon Sep 17 00:00:00 2001 From: Hao Xu Date: Tue, 10 Jun 2025 21:38:12 -0700 Subject: [PATCH 3/4] add compute example Signed-off-by: Hao Xu --- module_3_db/feature_repo/feature_store.yaml | 2 -- 1 file changed, 2 deletions(-) diff --git a/module_3_db/feature_repo/feature_store.yaml b/module_3_db/feature_repo/feature_store.yaml index 218634e..4451821 100644 --- a/module_3_db/feature_repo/feature_store.yaml +++ b/module_3_db/feature_repo/feature_store.yaml @@ -15,5 +15,3 @@ offline_store: spark.sql.parser.quotedRegexColumnNames: "true" spark.sql.session.timeZone: "UTC" entity_key_serialization_version: 2 -batch_engine: - From 7b3023d58f66e69b6e1777b915125efa18de874b Mon Sep 17 00:00:00 2001 From: Hao Xu Date: Tue, 10 Jun 2025 21:41:52 -0700 Subject: [PATCH 4/4] add compute example Signed-off-by: Hao Xu --- module_3_db/sample_db_notebook.ipynb | 8807 +------------------------- 1 file changed, 1 insertion(+), 8806 deletions(-) diff --git a/module_3_db/sample_db_notebook.ipynb b/module_3_db/sample_db_notebook.ipynb index 5b4b686..da03202 100644 --- a/module_3_db/sample_db_notebook.ipynb +++ b/module_3_db/sample_db_notebook.ipynb @@ -1,8806 +1 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "0c4646a7-272d-44ec-95fe-3480a267e173", - "showTitle": false, - "title": "" - } - }, - "outputs": [], - "source": [ - "%pip install feast[spark,aws,redis]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "d47268f0-01e8-442c-be33-97b11f444aaf", - "showTitle": false, - "title": "" - } - }, - "source": [ - "## Try fetching data from a data source" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "8a29f106-0008-462c-876e-1dfb13ecff08", - 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", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from feast import FeatureStore, RepoConfig\n", - "from feast.repo_config import RegistryConfig\n", - "from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig\n", - "from feast.infra.offline_stores.contrib.spark_offline_store.spark import SparkOfflineStoreConfig\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n", - "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) \n", - "\n", - "repo_config = RepoConfig(\n", - " registry=RegistryConfig(\n", - " registry_type=\"sql\", \n", - " path=\"postgresql://postgres:mysecretpassword@[YOUR-RDS-ENDPOINT:PORT]:5432/feast\"\n", - " ),\n", - " project=\"feast_demo\",\n", - " provider=\"local\",\n", - " offline_store=SparkOfflineStoreConfig(\n", - " spark_conf={\n", - " \"spark.ui.enabled\": \"false\",\n", - " \"spark.eventLog.enabled\": \"false\",\n", - " \"spark.sql.catalogImplementation\": \"hive\",\n", - " \"spark.sql.parser.quotedRegexColumnNames\": \"true\",\n", - " \"spark.sql.session.timeZone\": \"UTC\"\n", - " }\n", - " ),\n", - " online_store=DynamoDBOnlineStoreConfig(region=\"us-west-1\"),\n", - " entity_key_serialization_version=2\n", - ")\n", - "store = FeatureStore(config=repo_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "662c84bf-b655-4da2-8f59-4bf82b92241c", - "showTitle": false, - "title": "" - } - }, - "source": [ - "## Managing data pipelines\n", - "\n", - "### Part 1: Moving the latest feature values into a low latency store" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "83ad34fa-cbd3-4100-8c01-40720ddbc14e", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n", - "\n", - "user_transaction_amount_metrics:\n", - "Pulling latest features from spark offline store\n", - "\r", - " 0%| | 0/46 [00:00<?, ?it/s]\r", - " 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r", - "100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n", - "
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Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n\nuser_transaction_amount_metrics:\nPulling latest features from spark offline store\n\r 0%| | 0/46 [00:00<?, ?it/s]\r 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from datetime import datetime\n", - "store.materialize(\n", - " start_date=datetime(2022,4,20), \n", - " end_date=datetime(2022,4,21), \n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "1921ca3d-011a-4171-9d7b-9c55e5464f01", - "showTitle": false, - "title": "" - } - }, - "source": [ - "### Part 2: Computing + pushing streaming feature values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "b45f5951-bf01-48ae-8b76-3db0390508a8", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from datetime import timedelta\n", - "from pyspark.sql.functions import col, from_json, from_utc_timestamp, when\n", - "from pyspark.sql.types import StructType, StructField, StringType, DoubleType, TimestampType, BooleanType, IntegerType\n", - "\n", - "def raw_data_deserialization(df):\n", - " payload_schema = StructType([\n", - " StructField('user_id', StringType(), False),\n", - " StructField('transaction_id', StringType(), False),\n", - " StructField('category', StringType(), False),\n", - " StructField('amt', StringType(), False),\n", - " StructField('is_fraud', StringType(), False),\n", - " StructField('merchant', StringType(), False),\n", - " StructField('merch_lat', StringType(), False),\n", - " StructField('merch_long', StringType(), False),\n", - " StructField('timestamp', StringType(), False),\n", - " ])\n", - "\n", - " return (\n", - " df.selectExpr('cast (data as STRING) jsonData')\n", - " .select(from_json('jsonData', payload_schema).alias('payload'))\n", - " .select(\n", - " col('payload.user_id').alias('user_id'),\n", - " col('payload.transaction_id').alias('transaction_id'),\n", - " col('payload.category').alias('category'),\n", - " col('payload.amt').cast('double').alias('amt'),\n", - " col('payload.is_fraud').cast('long').alias('is_fraud'),\n", - " col('payload.merchant').alias('merchant'),\n", - " col('payload.merch_lat').cast('double').alias('merch_lat'),\n", - " col('payload.merch_long').cast('double').alias('merch_long'),\n", - " from_utc_timestamp('payload.timestamp', 'UTC').alias('timestamp')\n", - " )\n", - " )\n", - "\n", - "def ingest_and_deserialize_kinesis_df():\n", - " options = {\n", - " \"streamName\": \"[REDACTED STREAM NAME]\",\n", - " \"roleArn\": \"[REDACTED ROLE]\",\n", - " \"region\": \"us-west-2\",\n", - " \"shardFetchInterval\": \"30s\",\n", - " \"initialPosition\": \"latest\"\n", - " }\n", - " reader = spark.readStream.format(\"kinesis\").options(**options)\n", - " df = reader.load()\n", - " df = raw_data_deserialization(df)\n", - " watermark = \"{} seconds\".format(timedelta(hours=24).seconds)\n", - " df = df.withWatermark(\"timestamp\", watermark)\n", - " return df\n", - "\n", - "# 1) Ingest and deserialize streaming events from Kinesis stream\n", - "df = ingest_and_deserialize_kinesis_df()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "30c5835d-64fd-4439-b99c-a3c58ac7673a", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from pyspark.sql.functions import *\n", - "import pandas as pd\n", - "\n", - "# 2) Aggregate feature values\n", - "stream_agg = (\n", - " df.withWatermark(\"timestamp\", \"1 second\") \n", - " .groupBy(\"user_id\", window(timeColumn=\"timestamp\", windowDuration=\"1 day\", slideDuration=\"10 minutes\"))\n", - " .agg(\n", - " sum(\"amt\").alias(\"amt_sum_1d_10m\"),\n", - " avg(\"amt\").alias(\"amt_mean_1d_10m\")\n", - " )\n", - " .select(\"user_id\", col(\"window.end\").alias(\"timestamp\"), \"amt_sum_1d_10m\", \"amt_mean_1d_10m\")\n", - ")\n", - "\n", - "# 3) Push transformed features into Feast\n", - "from feast import FeatureStore\n", - "store = FeatureStore(config=repo_config)\n", - "\n", - "def send_to_feast(df, epoch):\n", - " pandas_df: pd.DataFrame = df.toPandas()\n", - " if pandas_df.empty:\n", - " return\n", - " \n", - " if \"timestamp\" in pandas_df:\n", - " # Filter out only for the latest window for the user_id\n", - " pandas_df = pandas_df.sort_values(by=[\"user_id\",\"timestamp\"], ascending=False).groupby(\"user_id\").nth(-1)\n", - " store.push(\"transactions_1d\", pandas_df)\n", - "\n", - "# 4) Launch streaming job to do all the above\n", - "query = (\n", - " stream_agg\n", - " .writeStream\n", - " .outputMode(\"append\") \n", - " .option(\"checkpointLocation\", \"/tmp/feast-workshop/\")\n", - " .trigger(processingTime=\"30 seconds\")\n", - " .foreachBatch(send_to_feast)\n", - " .start()\n", - ")\n", - "\n", - "# 5) (not shown) Orchestrate this job so it's self-healing. Monitor to ensure features land in Feast.\n", - "query.awaitTermination(timeout=30)\n", - "query.stop()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "a52ecca0-7135-44fd-80dd-1acf6bbc2503", - "showTitle": false, - "title": "" - } - }, - "source": [ - "## Retrieve features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "ae401586-7e84-4597-b736-e2764294fd24", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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"store = FeatureStore(config=repo_config)\n", - "\n", - "# To train a model, you need labeled events, and point in time correct features. Feast provides the features.\n", - "# Labeled events: historical log of user transactions (+ is_fraud)\n", - "# 1) Pull in user transaction features in that you need to train your model\n", - "entity_sql = f\"\"\"\n", - " SELECT\n", - " transaction_id,\n", - " user_id,\n", - " is_fraud, \n", - " to_timestamp(timestamp) as event_timestamp\n", - " FROM {store.get_data_source(\"transactions_source\").get_table_query_string()}\n", - " WHERE \n", - " timestamp BETWEEN '2022-04-20' AND '2022-04-21' AND\n", - " partition_0 = \"2022\" AND\n", - " partition_1 = \"04\" AND\n", - " partition_2 = \"20\"\n", - "\"\"\"\n", - "training_df = store.get_historical_features(\n", - " entity_df=entity_sql,\n", - " features=store.get_feature_service(\"model_v1\"),\n", - ").to_spark_df()\n", - "# model.fit(training_df)\n", - "display(training_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "1598dbdb-532a-496f-885e-56555d429002", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
amt_mean_1d_10m : [58.9284504132231]\n", - "amt_sum_1d_10m : [28521.36999999998]\n", - "user_id : ['user_268308151877']\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
amt_mean_1d_10m : [58.9284504132231]\namt_sum_1d_10m : [28521.36999999998]\nuser_id : ['user_268308151877']\n
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# 2) For an incoming transaction, pull the latest feature values for the associated user\n", - "# Note: Feast decoupled, so caller doesn't think about whether to pull this from a batch or streaming source\n", - "feature_vector = store.get_online_features(\n", - " features=store.get_feature_service(\"model_v1\"),\n", - " entity_rows=[\n", - " {\n", - " \"user_id\": \"user_268308151877\",\n", - " }\n", - " ],\n", - ").to_dict()\n", - "# model.predict(feature_vector)\n", - "\n", - "def print_online_features(feature_vector):\n", - " for key, value in sorted(features.items()):\n", - " print(key, \" : \", value)\n", - "\n", - "print_online_features(feature_vector)" - ] - } - ], - "metadata": { - "application/vnd.databricks.v1+notebook": { - "dashboards": [], - "language": "python", - "notebookMetadata": { - "mostRecentlyExecutedCommandWithImplicitDF": { - "commandId": 402528431658022, - "dataframes": [ - "_sqldf" - ] - }, - "pythonIndentUnit": 2 - }, - "notebookName": "Feast demo", - "notebookOrigID": 1254642919516165, - "widgets": {} - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - }, - "vscode": { - "interpreter": { - "hash": "7d634b9af180bcb32a446a43848522733ff8f5bbf0cc46dba1a83bede04bf237" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"0c4646a7-272d-44ec-95fe-3480a267e173","showTitle":false,"title":""}},"outputs":[],"source":["%pip install feast[spark,aws,redis]"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"d47268f0-01e8-442c-be33-97b11f444aaf","showTitle":false,"title":""}},"source":["## Try fetching data from a data source"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"8a29f106-0008-462c-876e-1dfb13ecff08","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["
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user_2683081518772022-04-07T22:31:28.924+0000101.8825.47
user_2683081518772022-04-07T22:31:41.513+0000109.821.96
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user_2683081518772022-04-07T22:41:20.686+0000491.9549.195
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"aggData":[],"aggError":"","aggOverflow":false,"aggSchema":[],"aggSeriesLimitReached":false,"aggType":"","arguments":{},"columnCustomDisplayInfos":{},"data":[["user_268308151877","2022-04-07T22:12:37.304+0000",1.79,1.79],["user_268308151877","2022-04-07T22:23:06.566+0000",10.870000000000001,5.4350000000000005],["user_268308151877","2022-04-07T22:26:15.216+0000",16.85,5.616666666666667],["user_268308151877","2022-04-07T22:31:28.924+0000",101.88,25.47],["user_268308151877","2022-04-07T22:31:41.513+0000",109.8,21.96],["user_268308151877","2022-04-07T22:39:28.687+0000",214.13,35.68833333333333],["user_268308151877","2022-04-07T22:39:42.791+0000",275.17,39.31],["user_268308151877","2022-04-07T22:39:48.833+0000",355.01,44.37625],["user_268308151877","2022-04-07T22:40:42.840+0000",466.32,51.81333333333333],["user_268308151877","2022-04-07T22:41:20.686+0000",491.95,49.195]],"datasetInfos":[],"dbfsResultPath":null,"isJsonSchema":true,"metadata":{},"overflow":false,"plotOptions":{"customPlotOptions":{},"displayType":"table","pivotAggregation":null,"pivotColumns":null,"xColumns":null,"yColumns":null},"removedWidgets":[],"schema":[{"metadata":"{}","name":"user_id","type":"\"string\""},{"metadata":"{}","name":"timestamp","type":"\"timestamp\""},{"metadata":"{}","name":"amt_sum_1d_10m","type":"\"double\""},{"metadata":"{}","name":"amt_mean_1d_10m","type":"\"double\""}],"type":"table"}},"output_type":"display_data"}],"source":["%sql\n","SELECT \n"," user_id,\n"," to_timestamp(timestamp) as timestamp,\n"," SUM(amt) OVER (PARTITION BY user_id ORDER BY to_timestamp(timestamp) RANGE BETWEEN INTERVAL 1 day PRECEDING AND CURRENT ROW) as amt_sum_1d_10m,\n"," AVG(amt) OVER (PARTITION BY user_id ORDER BY to_timestamp(timestamp) RANGE BETWEEN INTERVAL 1 day PRECEDING AND CURRENT ROW) as amt_mean_1d_10m\n","FROM demo_fraud_v2.transactions \n","WHERE \n"," partition_0 = \"2022\" AND\n"," partition_1 = \"04\"\n","limit 10"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"37baef9e-ffac-4cf9-ab6c-778e0321d544","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from feast import FeatureStore, RepoConfig\n","from feast.repo_config import RegistryConfig\n","from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig\n","from feast.infra.offline_stores.contrib.spark_offline_store.spark import SparkOfflineStoreConfig\n","import warnings\n","warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n","warnings.filterwarnings(\"ignore\", category=RuntimeWarning) \n","\n","repo_config = RepoConfig(\n"," registry=RegistryConfig(\n"," registry_type=\"sql\", \n"," path=\"postgresql://postgres:mysecretpassword@[YOUR-RDS-ENDPOINT:PORT]:5432/feast\"\n"," ),\n"," project=\"feast_demo\",\n"," provider=\"local\",\n"," offline_store=SparkOfflineStoreConfig(\n"," spark_conf={\n"," \"spark.ui.enabled\": \"false\",\n"," \"spark.eventLog.enabled\": \"false\",\n"," \"spark.sql.catalogImplementation\": \"hive\",\n"," \"spark.sql.parser.quotedRegexColumnNames\": \"true\",\n"," \"spark.sql.session.timeZone\": \"UTC\"\n"," }\n"," ),\n"," online_store=DynamoDBOnlineStoreConfig(region=\"us-west-1\"),\n"," entity_key_serialization_version=2\n",")\n","store = FeatureStore(config=repo_config)"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"662c84bf-b655-4da2-8f59-4bf82b92241c","showTitle":false,"title":""}},"source":["## Managing data pipelines\n","\n","### Part 1: Moving the latest feature values into a low latency store"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"83ad34fa-cbd3-4100-8c01-40720ddbc14e","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n","\n","user_transaction_amount_metrics:\n","Pulling latest features from spark offline store\n","\r 0%| | 0/46 [00:00<?, ?it/s]\r 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
Materializing 1 feature views from 2022-04-20 00:00:00+00:00 to 2022-04-21 00:00:00+00:00 into the dynamodb online store.\n\nuser_transaction_amount_metrics:\nPulling latest features from spark offline store\n\r 0%| | 0/46 [00:00<?, ?it/s]\r 54%|█████████████████████████████████▋ | 25/46 [00:00<00:00, 183.53it/s]\r100%|██████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 275.02it/s]\n
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from datetime import datetime\n","store.materialize(\n"," start_date=datetime(2022,4,20), \n"," end_date=datetime(2022,4,21), \n",")"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"1921ca3d-011a-4171-9d7b-9c55e5464f01","showTitle":false,"title":""}},"source":["### Part 2: Computing + pushing streaming feature values"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"b45f5951-bf01-48ae-8b76-3db0390508a8","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from datetime import timedelta\n","from pyspark.sql.functions import col, from_json, from_utc_timestamp, when\n","from pyspark.sql.types import StructType, StructField, StringType, DoubleType, TimestampType, BooleanType, IntegerType\n","\n","def raw_data_deserialization(df):\n"," payload_schema = StructType([\n"," StructField('user_id', StringType(), False),\n"," StructField('transaction_id', StringType(), False),\n"," StructField('category', StringType(), False),\n"," StructField('amt', StringType(), False),\n"," StructField('is_fraud', StringType(), False),\n"," StructField('merchant', StringType(), False),\n"," StructField('merch_lat', StringType(), False),\n"," StructField('merch_long', StringType(), False),\n"," StructField('timestamp', StringType(), False),\n"," ])\n","\n"," return (\n"," df.selectExpr('cast (data as STRING) jsonData')\n"," .select(from_json('jsonData', payload_schema).alias('payload'))\n"," .select(\n"," col('payload.user_id').alias('user_id'),\n"," col('payload.transaction_id').alias('transaction_id'),\n"," col('payload.category').alias('category'),\n"," col('payload.amt').cast('double').alias('amt'),\n"," col('payload.is_fraud').cast('long').alias('is_fraud'),\n"," col('payload.merchant').alias('merchant'),\n"," col('payload.merch_lat').cast('double').alias('merch_lat'),\n"," col('payload.merch_long').cast('double').alias('merch_long'),\n"," from_utc_timestamp('payload.timestamp', 'UTC').alias('timestamp')\n"," )\n"," )\n","\n","def ingest_and_deserialize_kinesis_df():\n"," options = {\n"," \"streamName\": \"[REDACTED STREAM NAME]\",\n"," \"roleArn\": \"[REDACTED ROLE]\",\n"," \"region\": \"us-west-2\",\n"," \"shardFetchInterval\": \"30s\",\n"," \"initialPosition\": \"latest\"\n"," }\n"," reader = spark.readStream.format(\"kinesis\").options(**options)\n"," df = reader.load()\n"," df = raw_data_deserialization(df)\n"," watermark = \"{} seconds\".format(timedelta(hours=24).seconds)\n"," df = df.withWatermark(\"timestamp\", watermark)\n"," return df\n","\n","# 1) Ingest and deserialize streaming events from Kinesis stream\n","df = ingest_and_deserialize_kinesis_df()"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"30c5835d-64fd-4439-b99c-a3c58ac7673a","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["from pyspark.sql.functions import *\n","import pandas as pd\n","\n","# 2) Aggregate feature values\n","stream_agg = (\n"," df.withWatermark(\"timestamp\", \"1 second\") \n"," .groupBy(\"user_id\", window(timeColumn=\"timestamp\", windowDuration=\"1 day\", slideDuration=\"10 minutes\"))\n"," .agg(\n"," sum(\"amt\").alias(\"amt_sum_1d_10m\"),\n"," avg(\"amt\").alias(\"amt_mean_1d_10m\")\n"," )\n"," .select(\"user_id\", col(\"window.end\").alias(\"timestamp\"), \"amt_sum_1d_10m\", \"amt_mean_1d_10m\")\n",")\n","\n","# 3) Push transformed features into Feast\n","from feast import FeatureStore\n","store = FeatureStore(config=repo_config)\n","\n","def send_to_feast(df, epoch):\n"," pandas_df: pd.DataFrame = df.toPandas()\n"," if pandas_df.empty:\n"," return\n"," \n"," if \"timestamp\" in pandas_df:\n"," # Filter out only for the latest window for the user_id\n"," pandas_df = pandas_df.sort_values(by=[\"user_id\",\"timestamp\"], ascending=False).groupby(\"user_id\").nth(-1)\n"," store.push(\"transactions_1d\", pandas_df)\n","\n","# 4) Launch streaming job to do all the above\n","query = (\n"," stream_agg\n"," .writeStream\n"," .outputMode(\"append\") \n"," .option(\"checkpointLocation\", \"/tmp/feast-workshop/\")\n"," .trigger(processingTime=\"30 seconds\")\n"," .foreachBatch(send_to_feast)\n"," .start()\n",")\n","\n","# 5) (not shown) Orchestrate this job so it's self-healing. Monitor to ensure features land in Feast.\n","query.awaitTermination(timeout=30)\n","query.stop()"]},{"cell_type":"markdown","metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"a52ecca0-7135-44fd-80dd-1acf6bbc2503","showTitle":false,"title":""}},"source":["## Retrieve features"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"ae401586-7e84-4597-b736-e2764294fd24","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["
transaction_iduser_idis_fraudevent_timestampamt_sum_1d_10mamt_mean_1d_10m
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18f4311b646729b4bb21c1a6bdec5307user_91797546299802022-04-20T16:29:38.415+000014541.38999999999264.91691964285711
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81f19b2159a89f76b9b34d327b2a745buser_46999844157102022-04-20T09:12:18.334+000015585.9899999999958.59394736842102
38130b635b6a08b989c78ff56dfb3d36user_2699081696802022-04-20T09:13:01.830+000031919.29999999999289.40980392156861
a387abebea458d78652615a642e288cbuser_2699081696802022-04-20T06:58:21.632+000023765.70000000000486.10760869565219
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7109e4f5655523f3ce7287ebeb04a99fuser_64478719978602022-04-20T12:32:39.043+000015221.98999999999855.963198529411756
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1c1609e6d9af27beb77d1a70c5d9caa8user_88424038724202022-04-20T16:30:53.770+000090064.8600000000265.88504754937821
af066d802ee826916c38b8554dcad69auser_44575547432602022-04-20T19:23:12.173+000025334.9762.4014039408867
5d27a1a7ce2fda6e2b154c7fa76517b7user_46087796178702022-04-20T19:24:22.702+000023155.0656.613838630806846
0eb60a2203119d0abe64067abe191860user_65602017453702022-04-20T13:10:32.030+000029145.6999999999952.420323741007174
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84c4f42ed17fb5fe772f3ccb971d338euser_50256760468902022-04-20T07:59:10.471+000011688.77999999999550.38267241379308
Showing the first 1000 rows.
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feast import FeatureStore\n","store = FeatureStore(config=repo_config)\n","\n","# To train a model, you need labeled events, and point in time correct features. Feast provides the features.\n","# Labeled events: historical log of user transactions (+ is_fraud)\n","# 1) Pull in user transaction features in that you need to train your model\n","entity_sql = f\"\"\"\n"," SELECT\n"," transaction_id,\n"," user_id,\n"," is_fraud, \n"," to_timestamp(timestamp) as event_timestamp\n"," FROM {store.get_data_source(\"transactions_source\").get_table_query_string()}\n"," WHERE \n"," timestamp BETWEEN '2022-04-20' AND '2022-04-21' AND\n"," partition_0 = \"2022\" AND\n"," partition_1 = \"04\" AND\n"," partition_2 = \"20\"\n","\"\"\"\n","training_df = store.get_historical_features(\n"," entity_df=entity_sql,\n"," features=store.get_feature_service(\"model_v1\"),\n",").to_spark_df()\n","# model.fit(training_df)\n","display(training_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"inputWidgets":{},"nuid":"1598dbdb-532a-496f-885e-56555d429002","showTitle":false,"title":""}},"outputs":[{"data":{"text/html":["\n","
amt_mean_1d_10m : [58.9284504132231]\n","amt_sum_1d_10m : [28521.36999999998]\n","user_id : ['user_268308151877']\n","
"]},"metadata":{"application/vnd.databricks.v1+output":{"addedWidgets":{},"arguments":{},"data":"
amt_mean_1d_10m : [58.9284504132231]\namt_sum_1d_10m : [28521.36999999998]\nuser_id : ['user_268308151877']\n
","datasetInfos":[],"metadata":{},"removedWidgets":[],"type":"html"}},"output_type":"display_data"}],"source":["# 2) For an incoming transaction, pull the latest feature values for the associated user\n","# Note: Feast decoupled, so caller doesn't think about whether to pull this from a batch or streaming source\n","feature_vector = store.get_online_features(\n"," features=store.get_feature_service(\"model_v1\"),\n"," entity_rows=[\n"," {\n"," \"user_id\": \"user_268308151877\",\n"," }\n"," ],\n",").to_dict()\n","# model.predict(feature_vector)\n","\n","def print_online_features(feature_vector):\n"," for key, value in sorted(features.items()):\n"," print(key, \" : \", value)\n","\n","print_online_features(feature_vector)"]}],"metadata":{"application/vnd.databricks.v1+notebook":{"dashboards":[],"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":402528431658022,"dataframes":["_sqldf"]},"pythonIndentUnit":2},"notebookName":"Feast demo","notebookOrigID":1254642919516165,"widgets":{}},"kernelspec":{"display_name":"Python 3.8.10 64-bit ('python-3.8')","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.8.10"},"vscode":{"interpreter":{"hash":"7d634b9af180bcb32a446a43848522733ff8f5bbf0cc46dba1a83bede04bf237"}}},"nbformat":4,"nbformat_minor":0}