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3-ChangeDataCapture-example.py
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3-ChangeDataCapture-example.py
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# Databricks notebook source
# DBTITLE 1,Install package from PyPi
# MAGIC %pip install dbldatagen
# COMMAND ----------
# MAGIC %md ###Change Data Capture
# COMMAND ----------
# MAGIC %md #### Overview
# MAGIC
# MAGIC We'll generate a customer table, and write out the data.
# MAGIC
# MAGIC Then we generate changes for the table and show merging them in.
# COMMAND ----------
# MAGIC %sql
# MAGIC drop table customers1
# COMMAND ----------
BASE_PATH = '/tmp/dbldatagen/cdc/'
dbutils.fs.mkdirs(BASE_PATH)
customers1_location = BASE_PATH + "customers1"
# COMMAND ----------
# MAGIC %md Lets generate 10 million customers
# MAGIC
# MAGIC We'll add a timestamp for when the row was generated and a memo field to mark what operation added it
# COMMAND ----------
import dbldatagen as dg # lgtm [py/repeated-import]
import pyspark.sql.functions as F
spark.catalog.clearCache()
shuffle_partitions_requested = 8
partitions_requested = 32
data_rows = 10 * 1000 * 1000
spark.conf.set("spark.sql.shuffle.partitions", shuffle_partitions_requested)
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
spark.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", 20000)
uniqueCustomers = 10 * 1000000
dataspec = (dg.DataGenerator(spark, rows=data_rows, partitions=partitions_requested)
.withColumn("customer_id", "long", uniqueValues=uniqueCustomers)
.withColumn("name", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
.withColumn("alias", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
.withColumn("payment_instrument_type", values=['paypal', 'Visa', 'Mastercard',
'American Express', 'discover', 'branded visa',
'branded mastercard'],
random=True, distribution="normal")
.withColumn("int_payment_instrument", "int", minValue=0000, maxValue=9999, baseColumn="customer_id",
baseColumnType="hash", omit=True)
.withColumn("payment_instrument", expr="format_number(int_payment_instrument, '**** ****** *####')",
baseColumn="int_payment_instrument")
.withColumn("email", template=r'\\w.\\w@\\w.com|\\w-\\w@\\w')
.withColumn("email2", template=r'\\w.\\w@\\w.com')
.withColumn("ip_address", template=r'\\n.\\n.\\n.\\n')
.withColumn("md5_payment_instrument",
expr="md5(concat(payment_instrument_type, ':', payment_instrument))",
base_column=['payment_instrument_type', 'payment_instrument'])
.withColumn("customer_notes", text=dg.ILText(words=(1,8)))
.withColumn("created_ts", "timestamp", expr="now()")
.withColumn("modified_ts", "timestamp", expr="now()")
.withColumn("memo", expr="'original data'")
)
df1 = dataspec.build()
# write table
df1.write.format("delta").save(customers1_location)
# COMMAND ----------
# MAGIC %md ###lets generate a table definition for it
# COMMAND ----------
customers1_location = BASE_PATH + "customers1"
tableDefn=dataspec.scriptTable(name="customers1", location=customers1_location)
spark.sql(tableDefn)
# COMMAND ----------
# MAGIC %sql
# MAGIC -- lets check our table
# MAGIC
# MAGIC select * from customers1
# COMMAND ----------
# MAGIC %md ### Changes
# MAGIC
# MAGIC Lets generate some changes
# COMMAND ----------
import pyspark.sql.functions as F
start_of_new_ids = df1.select(F.max('customer_id')+1).collect()[0][0]
print(start_of_new_ids)
# todo - as sequence for random columns will restart from previous seeds , you will get repeated values on next generation operation
# want to use seed sequences so that new random data is not same as old data from previous runs
df1_inserts = (dataspec.clone()
.option("startingId", start_of_new_ids)
.withRowCount(10 * 1000)
.build()
.withColumn("memo", F.lit("insert"))
.withColumn("customer_id", F.expr(f"customer_id + {start_of_new_ids}"))
)
# read the written data - if we simply recompute, timestamps of original will be lost
df_original = spark.read.format("delta").load(customers1_location)
df1_updates = (df_original.sample(False, 0.1)
.limit(50 * 1000)
.withColumn("alias", F.lit('modified alias'))
.withColumn("modified_ts", F.expr('now()'))
.withColumn("memo", F.lit("update")))
df_changes = df1_inserts.union(df1_updates)
# randomize ordering
df_changes = (df_changes.withColumn("order_rand", F.expr("rand()"))
.orderBy("order_rand")
.drop("order_rand")
)
display(df_changes)
# COMMAND ----------
# MAGIC %md ###Now lets merge in the changes
# MAGIC
# MAGIC We can script the merge statement in the data generator
# COMMAND ----------
df_changes.dropDuplicates(["customer_id"]).createOrReplaceTempView("customers1_changes")
sqlStmt = dataspec.scriptMerge(tgtName="customers1", srcName="customers1_changes",
joinExpr="src.customer_id=tgt.customer_id",
updateColumns=["alias", "memo","modified_ts"],
updateColumnExprs=[ ("memo", "'updated on merge'"),
("modified_ts", "now()")
])
print(sqlStmt)
spark.sql(sqlStmt)
# COMMAND ----------
# MAGIC %md Lets examine our table again
# COMMAND ----------
# MAGIC %sql
# MAGIC -- lets check our table for updates
# MAGIC
# MAGIC select * from customers1 where created_ts != modified_ts
# COMMAND ----------
# MAGIC %sql
# MAGIC -- lets check our table for inserts
# MAGIC
# MAGIC select * from customers1 where memo = "insert"
# COMMAND ----------
dbutils.fs.rm(BASE_PATH, recurse=True)