Spark SQL listener to report lineage data to a variety of outputs, e.g. Amazon Kinesis. Heavily inspired by the Spark Atlas Connector, but intended to be more generic to help those who can't or won't use Atlas.
For a Spark SQL query that produces an output (e.g. writes data to a filesystem), the listener produces a message containing the following:
- Output details, e.g. type, output location, and format
- For each (top-level) output field, a list of inputs that contribute to it. Each input contains:
- Type, e.g.
hive - List of input fields that affect the output field, and how they affect it (e.g. via a join, filter, aggregation, projection, etc.)
- Type, e.g.
- Metadata, e.g. spark application name
Build the jar:
$ ./gradlew shadowJarCopy the output from build/lib/jars to your cluster. Note that you will need to have the appropriate AWS SDK jars in
your spark classpath if you mean to use the associated reporters.
The listener is configured via a file, lineage.properties, that needs to be in the class path of the spark driver,
e.g. by placing it in the spark config directory. Here's an example using the KinesisReporter:
org.chojin.spark.lineage.reporters=org.chojin.spark.lineage.reporters.KinesisReporter
org.chojin.spark.lineage.reporter.kinesis.stream=my-kinesis-stream
org.chojin.spark.lineage.reporter.kinesis.region=us-east-1
org.chojin.spark.lineage.reporter.kinesis.shard=0
org.chojin.spark.lineage.reporter.kinesis.compression=gzip
Then, configure spark.sql.queryExecutionListeners to include the SparkSqlLineageListener:
$ spark-shell \
--jars file:///path/to/spark-lineage-0.0.3-SNAPSHOT.jar \
--conf spark.sql.queryExecutionListeners=org.chojin.spark.lineage.SparkSqlLineageListenerConsider the following spark example:
val foo = spark.table("mydb.foo")
val bar = spark.table("mydb.bar")
foo
.join(bar, foo.col("bar_fk") === bar.col("pk"))
.groupBy('my_flag)
.agg(sum('amount) as 'amount_sum)
.write
.mode("overwrite")
.parquet("s3:///bucket/path/to/report")Once evaluated, the following JSON record is produced:
{
"output": {
"path": "s3://bucket/path/to/report",
"type": "fs",
"format": "Parquet"
},
"metadata": {
"appName": "Spark shell"
},
"outputKey": "fs-s3://bucket/path/to/report",
"fields": {
"my_flag": [
{
"type": "hive",
"name": "mydb.foo",
"fields": [
{
"name": "bar_fk",
"how": "join"
}
]
},
{
"type": "hive",
"name": "mydb.bar",
"fields": [
{
"name": "pk",
"how": "join"
},
{
"name": "groupby",
"how": "my_flag"
}
]
}
],
"amount_sum": [
{
"type": "hive",
"name": "mydb.foo",
"fields": [
{
"name": "bar_fk",
"how": "join"
},
{
"name": "amount",
"how": "aggregate"
}
]
},
{
"type": "hive",
"name": "mydb.bar",
"fields": [
{
"name": "pk",
"how": "join"
},
{
"name": "groupby",
"how": "my_flag"
}
]
}
]
}
}One or more reporter classes can be specified as a comma-separated list in the parameter
org.chojin.spark.lineage.reporters.
Class name: org.chojin.spark.lineage.reporters.KinesisReporter
Write an optionally compressed JSON blob to an AWS Kinesis stream.
Options start with the prefix org.chojin.spark.lineage.reporter.kinesis.
stream- stream nameregion- region (e.g.us-east-2)shard- shard/partition to which to write; multiple shards are not supported at this timecompression- optional compression to apply to the JSON blob; any standard spark compression codec is supported, e.g.gzip,deflate
Class name: org.chojin.spark.lineage.reporters.DynamodbReporter
Write a structured record to an AWS DynamoDB table.
Options start with the prefix org.chojin.spark.lineage.reporter.dynamodb.
table- table nameregion- region (e.g.us-east-2)json- iftrue, write a single attribute,json, containing the lineage info as a binary blobcompression- optional compression to apply to the JSON blob (only ifjson=true; any standard spark compression codec is supported, e.g.gzip,deflate
With json=false, and using the spark example above, the following DynamoDB record is produced:
{
"Item": {
"output": {
"M": {
"path": {
"S": "s3://bucket/path/to/report"
},
"type": {
"S": "fs"
},
"format": {
"S": "Parquet"
}
}
},
"metadata": {
"M": {
"appName": {
"S": "Spark shell"
}
}
},
"outputKey": {
"S": "fs-s3://bucket/path/to/report"
},
"fields": {
"M": {
"my_flag": {
"L": [
{
"M": {
"fields": {
"L": [
{
"M": {
"how": {
"S": "join"
},
"name": {
"S": "bar_fk"
}
}
}
]
},
"type": {
"S": "hive"
},
"name": {
"S": "mydb.foo"
}
}
},
{
"M": {
"fields": {
"L": [
{
"M": {
"how": {
"S": "join"
},
"name": {
"S": "pk"
}
}
},
{
"M": {
"how": {
"S": "groupby"
},
"name": {
"S": "my_flag"
}
}
}
]
},
"type": {
"S": "hive"
},
"name": {
"S": "mydb.bar"
}
}
}
]
},
"amount_sum": {
"L": [
{
"M": {
"fields": {
"L": [
{
"M": {
"how": {
"S": "join"
},
"name": {
"S": "bar_fk"
}
}
},
{
"M": {
"how": {
"S": "aggregate"
},
"name": {
"S": "amount"
}
}
}
]
},
"type": {
"S": "hive"
},
"name": {
"S": "mydb.foo"
}
}
},
{
"M": {
"fields": {
"L": [
{
"M": {
"how": {
"S": "join"
},
"name": {
"S": "pk"
}
}
},
{
"M": {
"how": {
"S": "groupby"
},
"name": {
"S": "my_flag"
}
}
}
]
},
"type": {
"S": "hive"
},
"name": {
"S": "mydb.bar"
}
}
}
]
}
}
}
},
"ScannedCount": 1,
"ConsumedCapacity": null
}This is just a side project for me, and I'm more than willing to review pull requests. I just ask that you do the following:
- Add unit tests
- Try to respect code conventions (e.g. spaces not tabs)
- Provide a useful commit message
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