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doc updates : updates to github doc index, use only last 4 digits of fake payment instruments (#63)
* updates * use only last 4 digits of fake credit card numbers * use only last 4 digits of fake credit card numbers
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‎README.md

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@@ -76,7 +76,8 @@ For example
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import dbldatagen as dg
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from pyspark.sql.types import IntegerType, FloatType, StringType
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df_spec = (dg.DataGenerator(spark, name="test_data_set1", rows=cls.row_count,
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data_rows=1000 * 1000
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df_spec = (dg.DataGenerator(spark, name="test_data_set1", rows=data_rows,
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partitions=4)
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.withIdOutput()
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.withColumn("r", FloatType(), expr="floor(rand() * 350) * (86400 + 3600)",

‎docs/USING_THE_APIS.md

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@@ -15,5 +15,8 @@ Note that for Sphinx source documents (.rst docs), correct rendering is only pro
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- [Using data ranges](source/DATARANGES.md)
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- [Using data distributions](source/DISTRIBUTIONS.md)
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- [Generating text data](source/textdata.rst)
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- [Repeatable data generation](source/repeatable_data_generation.rst)
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- [Generating CDC data](source/generating_cdc_data.rst)
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- [Multi-table data generation](source/multi_table_data.rst)
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- [Troubleshooting](source/troubleshooting.rst)
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- [License](source/license.rst)

‎docs/source/APIDOCS.md

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## Using SQL in data generation
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Any column specification can use arbitrary SQL expressions during data generation via the `expr` parameter.
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The following example shows generation of synthetic names, email addresses, payment instruments and
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use of a SQL expression to compute MD5 hashes of synthetic credit card numbers:
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The following example shows generation of synthetic names, email addresses and
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use of a SQL expression to compute MD5 hashes of hypothetical synthetic credit card :
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```python
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import dbldatagen as dg
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dataspec = (dg.DataGenerator(spark, rows=data_rows, partitions=8, randomSeedMethod="hash_fieldname")
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.withColumn("name", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
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.withColumn("payment_instrument_type", values=['paypal', 'visa', 'mastercard', 'amex'], random=True)
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.withColumn("payment_instrument", minValue=1000000, maxValue=10000000, template="dddd dddddd ddddd")
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.withColumn("payment_instrument_type", values=['paypal', 'visa', 'mastercard', 'amex'],
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random=True)
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.withColumn("int_payment_instrument", "int", minValue=0000, maxValue=9999,
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baseColumn="name",
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baseColumnType="hash", omit=True)
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.withColumn("payment_instrument",
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expr="format_number(int_payment_instrument, '**** ****** *####')",
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baseColumn="int_payment_instrument")
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.withColumn("email", template=r'\\w.\\w@\\w.com')
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.withColumn("md5_payment_instrument",
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expr="md5(concat(payment_instrument_type, ':', payment_instrument))",
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spark.conf.set("spark.sql.shuffle.partitions", shuffle_partitions_requested)
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dataspec = (
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dg.DataGenerator(spark, rows=data_rows, partitions=8, randomSeedMethod="hash_fieldname", randomSeed=42)
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dg.DataGenerator(spark, rows=data_rows, partitions=8, randomSeedMethod="hash_fieldname",
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randomSeed=42)
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.withColumn("name", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
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.withColumn("payment_instrument_type", values=['paypal', 'visa', 'mastercard', 'amex'],
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random=True)
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.withColumn("payment_instrument", minValue=1000000, maxValue=10000000,
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template="dddd dddddd ddddd")
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.withColumn("int_payment_instrument", "int", minValue=0000, maxValue=9999,
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baseColumn="name",
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baseColumnType="hash", omit=True)
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.withColumn("payment_instrument",
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expr="format_number(int_payment_instrument, '**** ****** *####')",
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baseColumn="int_payment_instrument")
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.withColumn("email", template=r'\\w.\\w@\\w.com')
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.withColumn("md5_payment_instrument",
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expr="md5(concat(payment_instrument_type, ':', payment_instrument))",

‎docs/source/extending_text_generation.rst

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fakerDataspec = (dg.DataGenerator(spark, rows=data_rows, partitions=partitions_requested,
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randomSeedMethod="hash_fieldname")
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.withColumn("name", percentNulls=0.1, text=FakerText("name") )
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.withColumn("payment_instrument", text=FakerText("credit_card_number" ))
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.withColumn("address", text=FakerText("address" ))
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.withColumn("email", text=FakerText("ascii_company_email") )
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.withColumn("ip_address", text=FakerText("ipv4_private" ))
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.withColumn("faker_text", text=FakerText("sentence", ext_word_list=my_word_list) )
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ip_address_generator = (lambda context, v : context.faker.ipv4_private())
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name_generator = (lambda context, v : context.faker.name())
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cc_generator = (lambda context, v : context.faker.credit_card_number())
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address_generator = (lambda context, v : context.faker.address())
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email_generator = (lambda context, v : context.faker.ascii_company_email())
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fakerDataspec = (dg.DataGenerator(spark, rows=data_rows, partitions=partitions_requested,
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randomSeedMethod="hash_fieldname")
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.withColumn("name",
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percentNulls=0.1,
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text=PyfuncText(name_generator , initFn=initFaker))
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.withColumn("payment_instrument",
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text=PyfuncText(cc_generator, initFn=initFaker))
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.withColumn("address",
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text=PyfuncText(address_generator, initFn=initFaker))
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.withColumn("email",
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text=PyfuncText(email_generator, initFn=initFaker))
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.withColumn("ip_address",

‎docs/source/generating_cdc_data.rst

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@@ -52,8 +52,10 @@ We'll add a timestamp for when the row was generated and a memo field to mark wh
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.withColumn("name", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
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.withColumn("alias", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
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.withColumn("payment_instrument_type", values=['paypal', 'Visa', 'Mastercard', 'American Express', 'discover', 'branded visa', 'branded mastercard'], random=True, distribution="normal")
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.withColumn("int_payment_instrument", "long", minValue=100000000000000, maxValue=999999999999999, baseColumn="customer_id", baseColumnType="hash", omit=True)
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.withColumn("payment_instrument", expr="format_number(int_payment_instrument, '#### ###### #####')", baseColumn="int_payment_instrument")
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.withColumn("int_payment_instrument", "int", minValue=0000, maxValue=9999, baseColumn="customer_id",
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baseColumnType="hash", omit=True)
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.withColumn("payment_instrument", expr="format_number(int_payment_instrument, '**** ****** *####')",
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baseColumn="int_payment_instrument")
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.withColumn("email", template=r'\\w.\\w@\\w.com|\\w-\\w@\\w')
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.withColumn("email2", template=r'\\w.\\w@\\w.com')
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.withColumn("ip_address", template=r'\\n.\\n.\\n.\\n')

‎tutorial/3-ChangeDataCapture-example.py

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uniqueCustomers = 10 * 1000000
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dataspec = (dg.DataGenerator(spark, rows=data_rows, partitions=partitions_requested)
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.withColumn("customer_id","long", uniqueValues=uniqueCustomers)
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.withColumn("customer_id", "long", uniqueValues=uniqueCustomers)
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.withColumn("name", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
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.withColumn("alias", percentNulls=0.01, template=r'\\w \\w|\\w a. \\w')
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.withColumn("payment_instrument_type", values=['paypal', 'Visa', 'Mastercard', 'American Express', 'discover', 'branded visa', 'branded mastercard'], random=True, distribution="normal")
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.withColumn("int_payment_instrument", "long", minValue=100000000000000, maxValue=999999999999999, baseColumn="customer_id", baseColumnType="hash", omit=True)
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.withColumn("payment_instrument", expr="format_number(int_payment_instrument, '#### ###### #####')", baseColumn="int_payment_instrument")
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.withColumn("payment_instrument_type", values=['paypal', 'Visa', 'Mastercard',
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'American Express', 'discover', 'branded visa',
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'branded mastercard'],
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random=True, distribution="normal")
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.withColumn("int_payment_instrument", "int", minValue=0000, maxValue=9999, baseColumn="customer_id",
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baseColumnType="hash", omit=True)
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.withColumn("payment_instrument", expr="format_number(int_payment_instrument, '**** ****** *####')",
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baseColumn="int_payment_instrument")
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.withColumn("email", template=r'\\w.\\w@\\w.com|\\w-\\w@\\w')
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.withColumn("email2", template=r'\\w.\\w@\\w.com')
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.withColumn("ip_address", template=r'\\n.\\n.\\n.\\n')

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