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How to Implement Write-Audit-Publish Pattern with Apache Iceberg on AWS using WAP id

Detailed implementation of Write-Audit-Publish (WAP) Data Quality Pattern in AWS using Apache Iceberg WAP ID i.e. for Apache Iceberg < 1.2.0.

Akashdeep Gupta

• 11 min read
How to Implement Write-Audit-Publish Pattern with Apache Iceberg on AWS using WAP id
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This blog will cover how to implement a Write-Audit-Publish (WAP) Data Quality Pattern in AWS using Apache Spark on EMR that uses AWS Glue Catalog as metastore for the Apache Iceberg table and AWS Athena for querying the table.

💡
All the code in this blog post can be found here.
❗
This blog uses Apache Iceberg version < 1.2.1 as this version doesn't have a branching feature. To see the implementation via branching, check here.

This blog uses EMR 6.11, comes with Apache Spark 3.3 and, supports Apache Iceberg version 1.2.0

What is Write-Audit-Publish?

Write-Audit-Publish (WAP) is a data quality pattern commonly used in the data engineering workflow that helps us validate datasets by allowing us to write data to a staged snapshot/branch of the production table and fix any issues before finally committing the data to the production tables.

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WAP using wap.id in action

Things to know before implementation

  • On setting write.wap.enabled=true in the Iceberg table property, all the data written in the table is in stage mode only i.e. written data won't be reflected in the table until published.
  • Iceberg tables write data into snapshots.
  • Snapshots in an Iceberg table can be seen by querying the snapshot metadata table.
  • Snapshot metadata table can be queried via <table-name>.snapshots within spark.sql or SELECT * FROM "dbname"."tablename$snapshots" via Athena.
  • Once write.wap.enabled=true is set on the table, Athena doesn't show the table DDL until it's UNSET again but you can still query the data as usual. More details on this are covered here.

Pre-requisites for following along

An EMR running with Iceberg Cluster along with Spark 3.3 and Iceberg version < 1.2.1
The data that I will be using in this blog post is NYC Green Taxi Trip Data

When you are creating an EMR Cluster make sure, you Edit Software Setting > Enter Configuration
[{
    "Classification":"iceberg-defaults",
    "Properties":{"iceberg.enabled":"true"}
}]

Software Configuration while Creating EMR

Preparing Table and Data for WAP

Let's start with setting up all the configurations that we need in SparkSession for this implementation to work.

%%configure
{
    "conf": {
      "spark.jars.packages": "com.amazon.deequ:deequ:2.0.3-spark-3.3",
      "spark.jars.excludes": "net.sourceforge.f2j:arpack_combined_all",
      "spark.sql.extensions": "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions",
      "spark.sql.catalog.glue": "org.apache.iceberg.spark.SparkCatalog",
        "spark.sql.catalog.glue.catalog-impl":"org.apache.iceberg.aws.glue.GlueCatalog",
        "spark.sql.catalog.glue.io-impl":"org.apache.iceberg.aws.s3.S3FileIO",
        "spark.sql.catalog.glue.warehouse":"s3://bucket-name/glue/warehouse"
    }
}

SparkSession Configuration on Jupyter Notebook.

In the case of a spark script, you can set these configurations while creating your SparkSession using SparkSession.builder.config("config-key", "config-value") or spark.conf.set("config-key", "config-value")
spark.jars.packages and spark.jars.excludes are not required for WAP implementation with Iceberg. I have added these jars that will be used to demonstrate Auditing with Pydeequ.

To understand all the Iceberg related configurations, check here

Let's create an Iceberg Table that we will consider as a main Production Table on which we will implement WAP to write some new data from the Source Table.

from pyspark.sql.functions import lit, col

# Reading NYC Green Taxi Data for Sep 2023
raw_data_path = "s3://my-data-bucket/raw_data/nyc_tlc"
green_df = spark.read.format("parquet").load(f"{raw_data_path}/green/sep2023/")

# adding year and month column
green_df = green_df.withColumn("month", lit(9)) \
        .withColumn("year", lit(2023))

Reading Sep 2023 Green Taxi Trip Data for creating Production Iceberg Table

# creating an Iceberg table in glue catalog within nyc_tlc database. default compression is taken as 'zstd'
prod_table = "glue.nyc_tlc.green_taxi_trips"
green_df.writeTo(prod_table).partitionedBy("year", "month") \
            .using("iceberg") \
            .tableProperty("format-version", "2") \
            .tableProperty("write.parquet.compression-codec", "snappy") \
            .create()

Writing data to create a Production Table called green_taxi_trips

## Check data into prod table
spark.table(prod_table).groupBy("year", "month").count().show()

Verifying data into Production Table

+----+-----+-----+
|year|month|count|
+----+-----+-----+
|2023|    9|65471|
+----+-----+-----+

Output

# checking snapshots for the table
spark.sql(f"select * from {prod_table}.snapshots").show(truncate=False)

checking table snapshots

+-----------------------+-------------------+---------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|committed_at           |snapshot_id        |parent_id|operation|manifest_list                                                                                                                               |summary                                                                                                                                                                                                                                                                                                                  |
+-----------------------+-------------------+---------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|2024-01-02 08:47:33.829|4002909337217387123|null     |append   |s3://<glue_db_path>/green_taxi_trips/metadata/snap-4002909337217387123-1-a0d0113f-ad2e-4edf-a7a5-3716ca6c897b.avro|{spark.app.id -> application_1704184584490_0001, added-data-files -> 1, added-records -> 65471, added-files-size -> 1514631, changed-partition-count -> 1, total-records -> 65471, total-files-size -> 1514631, total-data-files -> 1, total-delete-files -> 0, total-position-deletes -> 0, total-equality-deletes -> 0}|
+-----------------------+-------------------+---------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Output

Athena only supports Iceberg Table v2 tables. This is something to keep in mind if you are using Athena as query engine.
show create table nyc_tlc.green_taxi_trips

-- Result

CREATE TABLE nyc_tlc.green_taxi_trips (
  VendorID int,
  lpep_pickup_datetime timestamp,
  lpep_dropoff_datetime timestamp,
  store_and_fwd_flag string,
  RatecodeID bigint,
  PULocationID int,
  DOLocationID int,
  passenger_count bigint,
  trip_distance double,
  fare_amount double,
  extra double,
  mta_tax double,
  tip_amount double,
  tolls_amount double,
  ehail_fee double,
  improvement_surcharge double,
  total_amount double,
  payment_type bigint,
  trip_type bigint,
  congestion_surcharge double,
  month int,
  year int)
PARTITIONED BY (`year`, `month`)
LOCATION 's3://<glue_db_path>/nyc_tlc/green_taxi_trips'
TBLPROPERTIES (
  'table_type'='iceberg',
  'write_compression'='snappy'
);

Checking Prod Table Schema using Athena

Let's prepare data that will be written using WAP in green_taxi_trips table.

## Source data that will be written later in the table.
source_df = spark.read.parquet(f"{raw_data_path}/green/oct2023/")
source_df = source_df.withColumn("month", lit(10)) \
        .withColumn("year", lit(2023))

source_df.groupBy("year", "month").count().show()

Reading data that will serve as Source Table Data and will be written in green_taxi_trips

+----+-----+-----+
|year|month|count|
+----+-----+-----+
|2023|   10|66177|
+----+-----+-----+

Output for source_df

Alrighty everything is set to start with the WAP implementation now i.e.

  • Production Table: glue.nyc_tlc.green_taxi_trips
  • Source Data: data present in source_df

WAP Implementation

Further steps show how to implement different stages in WAP i.e. Write - Audit - Publish with a bit of explanation.

Write

This stage ensures that we can safely write data into a branch created from the prod table so it's in isolation from the current production workloads.

  1. Set write.wap.enabled=true in table properties for letting Iceberg table handle write using WAP Pattern
# Set write.wap.enabled=true for table to write data into WAP Pattern.

spark.sql(f"ALTER TABLE {prod_table} SET TBLPROPERTIES ('write.wap.enabled'='true')")

Setting write.wap.enabled=true for table to write data using the WAP pattern

  1. Setting wap.id in SparkSession that will be used to identify the staged snapshot
# As Branching is not available in Iceberg < 1.2.1, 
# generated a wap.id that will be used to identify the snapshot_id for new data written into table

import uuid
wap_id = uuid.uuid4().hex

# set wap.id in spark session to put it in the summary of snapshot
spark.conf.set("spark.wap.id", wap_id)

print(wap_id)

Generating and setting wap.id in current SparkSession

  1. Writing Source Data into Production Table
# Appending data into Iceberg Prod Table
source_df.writeTo(prod_table).append()

Writing Source data into Production Table

# checking snapshots for the table
spark.sql(f"select * from {prod_table}.snapshots").show(truncate=False)

Checking table snapshots and wap.id in the snapshot summarysnapshots

+-----------------------+-------------------+-------------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|committed_at           |snapshot_id        |parent_id          |operation|manifest_list                                                                                                                               |summary                                                                                                                                                                                                                                                                                                                                                               |
+-----------------------+-------------------+-------------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|2024-01-02 08:47:33.829|4002909337217387123|null               |append   |s3://<glue_db_path>/green_taxi_trips/metadata/snap-4002909337217387123-1-a0d0113f-ad2e-4edf-a7a5-3716ca6c897b.avro|{spark.app.id -> application_1704184584490_0001, added-data-files -> 1, added-records -> 65471, added-files-size -> 1514631, changed-partition-count -> 1, total-records -> 65471, total-files-size -> 1514631, total-data-files -> 1, total-delete-files -> 0, total-position-deletes -> 0, total-equality-deletes -> 0}                                             |
|2024-01-02 08:56:07.165|7270839791374367070|4002909337217387123|append   |s3://<glue_db_path>/green_taxi_trips/metadata/snap-7270839791374367070-1-26181e44-1f0d-47e2-8e88-febb7f2c1e7d.avro|{spark.app.id -> application_1704184584490_0001, wap.id -> fcd54f05ee4b4480a21c191cd5c17f39, added-data-files -> 1, added-records -> 66177, added-files-size -> 1519040, changed-partition-count -> 1, total-records -> 131648, total-files-size -> 3033671, total-data-files -> 2, total-delete-files -> 0, total-position-deletes -> 0, total-equality-deletes -> 0}|
+-----------------------+-------------------+-------------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Output. Check wap.id in summary column

Let's verify the data in the Production Table

spark.table(prod_table).groupBy("year","month").count().show()

Verifying data in Production Table

+----+-----+-----+
|year|month|count|
+----+-----+-----+
|2023|    9|65471|
+----+-----+-----+

Output from Production Table

As mentioned before, the data that is written with write.wap.enabled=true is in staging mode. Let's verify the data in the snapshot written in the previous write.

# Getting the snapshot_id corresponding to wap_id that was added in Spark Session.
from pyspark.sql.functions import col
staged_data_snap_id = spark.sql(f"select snapshot_id, summary['wap.id'] as wap_id from {prod_table}.snapshots").filter(col("wap_id") == wap_id).collect()[0]['snapshot_id']

# Reading data in the staged snapshot
staged_data = spark.read.option("snapshot-id", staged_data_snap_id).table(prod_table)

staged_data.groupBy("year","month").count().show()

Checking data in the staged snapshot

+----+-----+-----+
|year|month|count|
+----+-----+-----+
|2023|   10|66177|
|2023|    9|65471|
+----+-----+-----+

Output from staged_snapshot

Audit

This stage enables us to understand the quality of our newly inserted data. During this stage, we can run various data quality checks such as inspecting NULL values, identifying duplicated data, etc.

You can define any of the DQ Processes here via some code, third-party provider, or any other service. A few of such options are:

I will be using the first option here via PyDeequ. To learn more about PyDeequ and how it works, you can refer to this blog post.
Let's say our DQ requirement is:
- Completeness criteria for VendorID should be 1.0 i.e. there are no `null`.
- total_amount shouldn't be negative
- payment_type should be discrete numbers between 1 and 6.

import os

# Setting SPARK_VERSION in environment variable for Pydeequ
os.environ['SPARK_VERSION'] = '3.3'

from pydeequ.checks import *
from pydeequ.verification import *

# Check represents a list of constraints that can be applied to a provided Spark Dataframe
vendorID_checks = Check(spark, CheckLevel.Error, "VendorID Checks")
payment_type_checks = Check(spark, CheckLevel.Error, "payment_type Checks")
tot_amt_checks = Check(spark, CheckLevel.Warning, "total_amount Checks")


checkResult = VerificationSuite(spark) \
    .onData(staged_data) \
    .addCheck(vendorID_checks.isComplete("VendorID")) \
    .addCheck(payment_type_checks.isContainedIn("payment_type", ['1','2','3','4','5','6'])) \
    .addCheck(tot_amt_checks.isNonNegative("total_amount")) \
    .run()

print(f"Verification Run Status: {checkResult.status}")
checkResult_df = VerificationResult.checkResultsAsDataFrame(spark, checkResult)
checkResult_df.show(truncate=False)

Checking Data Quality using PyDeequ

+-------------------+-----------+------------+--------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-------------------------------------------------------------------+
|check              |check_level|check_status|constraint                                                                                                                                        |constraint_status|constraint_message                                                 |
+-------------------+-----------+------------+--------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-------------------------------------------------------------------+
|VendorID Checks    |Error      |Success     |CompletenessConstraint(Completeness(VendorID,None))                                                                                               |Success          |                                                                   |
|payment_type Checks|Error      |Success     |ComplianceConstraint(Compliance(payment_type contained in 1,2,3,4,5,6,`payment_type` IS NULL OR `payment_type` IN ('1','2','3','4','5','6'),None))|Success          |                                                                   |
|total_amount Checks|Warning    |Warning     |ComplianceConstraint(Compliance(total_amount is non-negative,COALESCE(CAST(total_amount AS DECIMAL(20,10)), 0.0) >= 0,None))                      |Failure          |Value: 0.9968780384054449 does not meet the constraint requirement!|
+-------------------+-----------+------------+--------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+-------------------------------------------------------------------+

PyDeequ Data Quality Results

As we can see, one of the DQ Validations failed as total_amount column has some negative values.
Based on this result and discussion with stakeholders, we can decide whether we want to Publish the data to the Production table or not.

Scenario 1: Let's say as per discussion with stakeholders, we don't want to publish the data in Production because DQ Checks have failed.

spark.sql(f"CALL glue.system.expire_snapshots(table => '{prod_table}', snapshot_ids => ARRAY({staged_data_snap_id}))")

Rejecting/Expiring the snapshot based on DQ results

Scenario 2: Let's say as per discussion with stakeholders, we can clean the data by filtering out all such records that have negative total_amount and write the data with a negative total_amount to some bad data location.

# filtering out the non-negative data from staged_df
cleaned_df = staged_data.filter(col("total_amount") >= 0)

# writing bad data into an S3 location that can be looked on later.
bad_data_df = staged_data.filter(col("total_amount") < 0)
bad_data_df.write.partitionedBy("year", "month").parquet("bad-data-s3-location")

Filtering out records having total_amount < 0 to make it clean

Once we have cleaned the data, we can rewrite this into the table.

# write cleaned data into prod table.
cleaned_df.writeTo(prod_table).overwritePartitions()

Writing cleaned data into table.

Now the important thing to notice here is that as the data is re-written on top of staged snapshot, a new snapshot_id for this write is generated . This new snapshot_id will be the actual data, that needs to be published in the Production Table.

Let's look into the snapshot metadata table to understand what I mean.

# Checking the snapshots again
spark.sql(f"select * from {prod_table}.snapshots").show(truncate=False)

Checking snapshot_ids corresponding to the wap_id

+-----------------------+-------------------+-------------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|committed_at           |snapshot_id        |parent_id          |operation|manifest_list                                                                                                                               |summary                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+-----------------------+-------------------+-------------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|2024-01-02 08:47:33.829|4002909337217387123|null               |append   |s3://<glue_db_location>/green_taxi_trips/metadata/snap-4002909337217387123-1-a0d0113f-ad2e-4edf-a7a5-3716ca6c897b.avro|{spark.app.id -> application_1704184584490_0001, added-data-files -> 1, added-records -> 65471, added-files-size -> 1514631, changed-partition-count -> 1, total-records -> 65471, total-files-size -> 1514631, total-data-files -> 1, total-delete-files -> 0, total-position-deletes -> 0, total-equality-deletes -> 0}                                                                                                                                                            |
|2024-01-02 08:56:07.165|7270839791374367070|4002909337217387123|append   |s3://<glue_db_location>/green_taxi_trips/metadata/snap-7270839791374367070-1-26181e44-1f0d-47e2-8e88-febb7f2c1e7d.avro|{spark.app.id -> application_1704184584490_0001, wap.id -> fcd54f05ee4b4480a21c191cd5c17f39, added-data-files -> 1, added-records -> 66177, added-files-size -> 1519040, changed-partition-count -> 1, total-records -> 131648, total-files-size -> 3033671, total-data-files -> 2, total-delete-files -> 0, total-position-deletes -> 0, total-equality-deletes -> 0}                                                                                                               |
|2024-01-02 09:14:03.382|8254249659682978316|4002909337217387123|overwrite|s3://<glue_db_location>/green_taxi_trips/metadata/snap-8254249659682978316-1-70060bcd-8b81-4e00-b26c-92ecca1145bd.avro|{spark.app.id -> application_1704184584490_0001, replace-partitions -> true, wap.id -> fcd54f05ee4b4480a21c191cd5c17f39, added-data-files -> 2, deleted-data-files -> 1, added-records -> 131237, deleted-records -> 65471, added-files-size -> 3022251, removed-files-size -> 1514631, changed-partition-count -> 2, total-records -> 131237, total-files-size -> 3022251, total-data-files -> 2, total-delete-files -> 0, total-position-deletes -> 0, total-equality-deletes -> 0}|
+-----------------------+-------------------+-------------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Output from snapshot metadata table

Things to observe in above output:

  • Now we have 2 snapshot_id (7270839791374367070 and 8254249659682978316) corresponding to the wap.id i.e. fcd54f05ee4b4480a21c191cd5c17f39 written at different times i.e. commited_at
  • Data that needs to be published in the Production Table should be the cleaned data i.e. of snapshot_id=8254249659682978316

Publish

This stage allows data to be made available to the production tables for consumption by the downstream analytical applications.

Once the Auditing is done and DQ is as expected or fixed. We can Publish the final snapshot in the Production table by calling an Iceberg Store Procedure called system.cherrypick_snapshot.

To publish the snapshot from the above scenario, we need to get the latest snapshot_id based on commited_at timestamp for the wap.id that we used to perform the write.

# getting the latest snapshot_id
udpated_data_snap_id = spark.sql(f"select snapshot_id, summary['wap.id'] as wap_id from {prod_table}.snapshots").filter(col("wap_id") == wap_id).orderBy(col("committed_at").desc()).collect()[0]['snapshot_id']

# Publishing the this new snapshot_id into Production table
spark.sql(f"CALL glue.system.cherrypick_snapshot('{prod_table}', {udpated_data_snap_id})").show()

Getting latest snapshot_id and publishing it into the Production Table

+-------------------+-------------------+
| source_snapshot_id|current_snapshot_id|
+-------------------+-------------------+
|8254249659682978316|8254249659682978316|
+-------------------+-------------------+

Output after running cherrypick_snapshot to Publish data into the Production Table

Let's verify the records in the Production Table now from AWS Athena

select "year", "month", count(*) from nyc_tlc.green_trips_data group by 1,2

Validating the final data in Athena Table

Output from Athena Table

Finally, to make the DDL queryable again from Athena, UNSET the table property write.wap.enabled . The reason for doing this is explained in detail in this blog post.

# unset from table properties so that create table ddl can be seen again on Athena.
spark.sql(f"ALTER TABLE {prod_table} UNSET TBLPROPERTIES ('write.wap.enabled')")

Unsetting write.wap.enabled from the Iceberg Table

That's it for this one folks..!! Now you know how to implement WAP Pattern on AWS for Iceberg version < 1.2.1

If you have read until here, please leave a comment below, and any feedback is highly appreciated. See you in the next post..!!! 😊

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