This tool enables you to easily visualize column-level reference relationship (so called data lineage) between tables/views stored in Spark SQL. The feature is useful for understanding your data transformation workflow in SQL/DataFrame and deciding which tables/views should be cached and which ones should not to optimize performance/storage trade-off.
For instance, a diagram above is generated by this tool and it is the transformation workflow of spark-data-repair-plugin.
The light blue nodes mean cached tables/views/plans and tables/views might be worth being cached if they are referenced by more than one plan
(e.g., the freq_attr_stats__5937180596123253 view in a little left side of the center is cached because the four plans reference the view).
For more visualization examples, please see a tpcds-flow-tests folder
and it includes data lineage for the TPC-DS queries.
How to Visualize Data Lineage for Your Tables/Views
The tool has interfaces for Scala and Python. If you use Python, how to generate data lineage is as follows:
# You need to check out this repository first
$ git clone https://github.com/maropu/spark-sql-flow-plugin.git
$ cd spark-sql-flow-plugin
# NOTE: a script 'bin/python' automatically creates a 'conda' virtual env to install required
# modules. If you install them by yourself (e.g., pip install -r bin/requirements.txt),
# you need to define a env 'CONDA_DISABLED' like 'CONDA_DISABLED=1 ./bin/python'
$ ./bin/python
Welcome to
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/__ / .__/\_,_/_/ /_/\_\ version 3.2.0
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Using Python version 3.7.10 (default, Jun 4 2021 14:48:32)
# Defines some views for this example
>>> sql("CREATE TABLE TestTable (key INT, value INT)")
>>> sql("CREATE TEMPORARY VIEW TestView1 AS SELECT key, SUM(value) s FROM TestTable GROUP BY key")
>>> sql("CACHE TABLE TestView1")
>>> sql("CREATE TEMPORARY VIEW TestView2 AS SELECT t.key, t.value, v.s FROM TestTable t, TestView1 v WHERE t.key = v.key")
# Generates a Graphviz dot file to represent reference relationships between views
>>> from sqlflow import save_data_lineage
>>> save_data_lineage(output_dir_path="/tmp/sqlflow-output")
$ ls /tmp/sqlflow-output
sqlflow.dot sqlflow.svg
sqlflow.dot is a Graphviz dot file and you can use the dot command or GraphvizOnline
to convert it into a specified image, e.g., SVG and PNG.
If dot already installed on your machine, a SVG-formatted image (sqlflow.svg in this example)
is automatically generated by default:
If contracted is set to true in save_data_lineage, it generates the compact form
of a data lineage diagram that omits plan nodes as follows:
>>> save_data_lineage(output_dir_path="/tmp/sqlflow-output", contracted = true)
In case of Scala, you can use SQLFlow.saveAsSQLFlow instead to generate a dot file as follows:
$ ./bin/spark-shell
Welcome to
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/__ / .__/\_,_/_/ /_/\_\ version 3.2.0
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scala> sql("CREATE TABLE TestTable (key INT, value INT)")
scala> sql("CREATE TEMPORARY VIEW TestView1 AS SELECT key, SUM(value) s FROM TestTable GROUP BY key")
scala> sql("CACHE TABLE TestView1")
scala> sql("CREATE TEMPORARY VIEW TestView2 AS SELECT t.key, t.value, v.s FROM TestTable t, TestView1 v WHERE t.key = v.key")
scala> import org.apache.spark.sql.flow.SQLFlow
scala> SQLFlow.saveAsSQLFlow(Map("outputDirPath" -> "/tmp/sqlflow-output"))
Automatic Tracking with Python '@auto_tracking' Decorator
If you have a set of the functions that take and return DataFrame for data transformation,
a Python decorator @auto_tracking is useful to track data lineage automatically:
>>> from pyspark.sql import functions as f
>>> from sqlflow import auto_tracking, save_data_lineage
>>> @auto_tracking
... def transform_alpha(df):
... return df.selectExpr('id % 3 AS key', 'id % 5 AS value')
...
>>> @auto_tracking
... def transform_beta(df):
... return df.groupBy('key').agg(f.expr('collect_set(value)').alias('value'))
...
>>> @auto_tracking
... def transform_gamma(df):
... return df.selectExpr('explode(value)')
...
# Applies a chain of transformation functions
>>> transform_gamma(transform_beta(transform_alpha(spark.range(10))))
DataFrame[col: bigint]
>>> save_data_lineage(output_dir_path='/tmp/sqlflow-output', contracted=True)
Automatically tracked data lineage is as follows:
Exports Data Lineage into Other Systems
Adjacency List
Most graph processing libraries (e.g., Python NetworkX)
can load a adjacency list file that includes a source node name of an edge
and its destination node name in each line. To generate it for exporting data lineage into these libraries,
a Python user can set a graph_sink='adjacency_list' in save_data_lineage.
Note that the output file only contains coarse-grained reference relationships between tables/views/plans
because it is difficult to represent column-level references in an adjacency list.
# NOTE: Valid `graph_sink` value is `graphviz` or `adjacency_list` (`graphviz` by default)
>>> from sqlflow import save_data_lineage
>>> save_data_lineage(output_dir_path='/tmp/sqlflow-output', graph_sink='adjacency_list', contracted=False, options='sep=,')
$ cat /tmp/sqlflow-output/sqlflow.lst
default.testtable,Aggregate_4
Project_3,testview2
Join_Inner_2,Project_3
Aggregate_4,testview1
default.testtable,Filter_0
Filter_1,Join_Inner_2
testview1,Filter_1
Filter_0,Join_Inner_2
To generate an adjacency list file of data lineage in Scala, you can specify AdjacencyListSink
in SQLFlow.saveAsSQLFlow as follows:
scala> import org.apache.spark.sql.flow.sink.AdjacencyListSink
scala> SQLFlow.saveAsSQLFlow(Map("outputDirPath" -> "/tmp/sqlflow-output"), graphSink=AdjacencyListSink(sep = ","))
See a resources/networkx_example.ipynb example for how to load it into Python NetowrkX.
Neo4j Aura (Experimental feature)
To share data lineage with others, it is useful to export it into Neo4j Aura, a fully-managed graph dtabase service:
>>> from sqlflow import export_data_lineage_into
>>> neo4jaura_options = {'uri': 'neo4j+s://<your Neo4j database uri>', 'user': '<user name>', 'passwd': '<password>'}
>>> export_data_lineage_into("neo4jaura", options=neo4jaura_options)
For instance, the exported data lineage of spark-data-repair-plugin (the top-most example)
is represented below on your Neo4j browser:
To understand which views are referenced multiple times by plans, you can run a CYPHER query below for listing up the views. Therefore, the result views can be candidiates to be cached.
// Lists up the `View` nodes that are referenced multiple times by `Plan` nodes
MATCH (n:LeafPlan)-[t:transformInto]->(:Plan)
WITH n, sum(size(t.dstNodeIds)) as refs
WHERE refs >= 2
RETURN n.name
Another useful example is most-frequently query tracking; a transformInto relationship has the refCnt property
that is the number of references by queries, so you can select frequently-referenced transformInto paths by a query below:
// Selects the relationships whose reference count is more than 1
MATCH p=(s)-[:transformInto*1..]->(e)
WHERE (s:LeafPlan OR s:Table OR s:View) AND ALL(r IN relationships(p) WHERE size(r.dstNodeIds) >= 2)
MATCH (e)-[:transformInto]->(q:Query)
RETURN p, q
Other useful CYPHER queries can be found in resources/README.md.
Writes Your Custom Graph Formatter
SQLFlow extracts the column-level references of tables/views/plans as a sequence
of SQLFlowGraphNodes and SQLFlowGraphEdges internally.
Therefore, you can take extracted references and transform them into string data following your custom format:
scala> import org.apache.spark.sql.flow.{SQLFlow, SQLFlowGraphEdge, SQLFlowGraphNode}
scala> SQLFlow.printAsSQLFlow(contracted = true,
| (nodes: Seq[SQLFlowGraphNode], edges: Seq[SQLFlowGraphEdge]) => {
| s"""
| |List of nodes:
| |${nodes.map(n => s" => $n").mkString("\n")}
| |
| |List of edges:
| |${edges.map(e => s" => $e").mkString("\n")}
| """.stripMargin
| })
List of nodes:
=> name=`default.testtable`(`key`,`value`), type=table, cached=false
=> name=`testview2`(`key`,`value`,`s`), type=table, cached=false
=> name=`testview1`(`key`,`s`), type=table, cached=true
List of edges:
=> from=`default.testtable`(idx=0), to=`testview2`(idx=0)
=> from=`default.testtable`(idx=1), to=`testview2`(idx=1)
=> from=`testview1`(idx=0), to=`testview2`(idx=0)
=> from=`testview1`(idx=1), to=`testview2`(idx=2)
=> from=`default.testtable`(idx=0), to=`testview1`(idx=0)
=> from=`default.testtable`(idx=1), to=`testview1`(idx=1)
Audits queries to generate data lineage (Experimental feature)
SQLFlowListener below incrementally appends the column-level references of a SQL/DataFrame query
into a specified sink every it succeeds:
scala> import org.apache.spark.sql.flow.sink.Neo4jAuraSink
scala> val sink = Neo4jAuraSink("neo4j+s://<your Neo4j database uri>", "<user name>", "<password>")
scala> spark.sqlContext.listenerManager.register(SQLFlowListener(sink))
To load the listener when launching a Spark cluster, you can use Spark configurations:
./bin/spark-shell --conf spark.sql.queryExecutionListeners=org.apache.spark.sql.flow.Neo4jAuraSQLFlowListener \
--conf spark.sql.flow.Neo4jAuraSink.uri=neo4j+s://<your Neo4j database uri> \
--conf spark.sql.flow.Neo4jAuraSink.user=<user name> \
--conf spark.sql.flow.Neo4jAuraSink.password=<password>
Similar Technologies
- Datafold, Column-level lineage, https://www.datafold.com/column-level-lineage
- SQLFlow, Automated SQL data lineage analysis, https://www.gudusoft.com
- Prophecy, Spark: Column Level Lineage, https://medium.com/prophecy-io/spark-column-level-lineage-478c1fe4701d
- Collibra/SQLDep, Introducing Collibra Lineage - Automated Data Lineage, https://www.collibra.com/us/en/blog/introducing-collibra-lineage
- Tokern, Automate data engineering tasks with column-level data lineage, https://tokern.io
References
- Hui Miao et al., ProvDB: A System for Lifecycle Management of Collaborative Analysis Workflows, arXiv, arXiv:1610.04963, 2016.
- Joseph M. Hellerstein et al, Ground: A Data Context Service, CIDR, 2017.
- R. Castro Fernandez et al., Aurum: A Data Discovery System, Proceedings of ICDE, pp.1001-1012, 2018.
- Alon Halevy et al., Goods: Organizing Google's Datasets. Proceedings of SIGMOD, pp.795–806, 2016.
TODO
- Implements more graph exporters, e.g., for Apache Atlas and DataHub/Acryl Data (Issue#3)
- Tracks data lineage between table/views via
INSERTqueries (Issue#5) - Adds more CYPHER query examples in resources/README.md, e.g.,
- CYPHER query to group SQL/DataFrame queries by their similarities (alternative approach to store query logs is spark-query-log-plugin)
- Supports global temp views
Bug Reports
If you hit some bugs and have requests, please leave some comments on Issues or Twitter (@maropu).