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new 04c9583cecdf [SPARK-45833][SS][DOCS] Document the new introduction of
state data source
04c9583cecdf is described below
commit 04c9583cecdf929e8ac57eb07a8c53f488c47671
Author: Jungtaek Lim <kabhwan.opensou...@gmail.com>
AuthorDate: Tue Nov 28 17:05:11 2023 +0900
[SPARK-45833][SS][DOCS] Document the new introduction of state data source
### What changes were proposed in this pull request?
This PR proposes to add a new doc page describing the new data source,
`state data source`.
Worth noting that we explicitly mention the data source as experimental, so
that we do not close the opportunity to improve further if it's backward
incompatible.
### Why are the changes needed?
The data source is an user-facing one and would be evolved over time, hence
we'll need to document and publicize it.
### Does this PR introduce _any_ user-facing change?
Yes, doc change.
### How was this patch tested?
Built the docs directory with jekyll. Here are relevant screenshots.
<img width="1136" alt="스크린샷 2023-11-28 오후 5 02 47"
src="https://github.com/apache/spark/assets/1317309/b36fea49-89f0-484d-b90d-be069c589b8f";>
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #43920 from HeartSaVioR/SPARK-45833.
Authored-by: Jungtaek Lim <kabhwan.opensou...@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensou...@gmail.com>
---
docs/structured-streaming-programming-guide.md | 8 +
docs/structured-streaming-state-data-source.md | 248 +++++++++++++++++++++++++
2 files changed, 256 insertions(+)
diff --git a/docs/structured-streaming-programming-guide.md
b/docs/structured-streaming-programming-guide.md
index 547834c7f9e3..33b9453a18c3 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -2452,6 +2452,14 @@ Specifically for built-in HDFS state store provider,
users can check the state s
it is best if cache missing count is minimized that means Spark won't waste
too much time on loading checkpointed state.
User can increase Spark locality waiting configurations to avoid loading state
store providers in different executors across batches.
+#### State Data Source (Experimental)
+
+Apache Spark provides a streaming state related data source that provides the
ability to manipulate state stores in the checkpoint. Users can run the batch
query with State Data Source to get the visibility of the states for existing
streaming query.
+
+As of Spark 4.0, the data source only supports read feature. See [State Data
Source Integration Guide](structured-streaming-state-data-source.html) for more
details.
+
+NOTE: this data source is currently marked as experimental - source options
and the behavior (output) might be subject to change.
+
## Starting Streaming Queries
Once you have defined the final result DataFrame/Dataset, all that is left is
for you to start the streaming computation. To do that, you have to use the
`DataStreamWriter`
([Python](api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.html#pyspark.sql.streaming.DataStreamWriter)/[Scala](api/scala/org/apache/spark/sql/streaming/DataStreamWriter.html)/[Java](api/java/org/apache/spark/sql/streaming/DataStreamWriter.html)
docs)
diff --git a/docs/structured-streaming-state-data-source.md
b/docs/structured-streaming-state-data-source.md
new file mode 100644
index 000000000000..a9353861c532
--- /dev/null
+++ b/docs/structured-streaming-state-data-source.md
@@ -0,0 +1,248 @@
+---
+layout: global
+displayTitle: State Data Source Integration Guide
+title: State Data Source Integration Guide
+license: |
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+---
+
+State data source Guide in Structured Streaming (Experimental)
+
+## Overview
+
+State data source provides functionality to manipulate the state from the
checkpoint.
+
+As of Spark 4.0, state data source provides the read functionality with a
batch query. Additional functionalities including write is on the future
roadmap.
+
+NOTE: this data source is currently marked as experimental - source options
and the behavior (output) might be subject to change.
+
+## Reading state key-values from the checkpoint
+
+State data source enables reading key-value pairs from the state store in the
checkpoint, via running a separate batch query.
+Users can leverage the functionality to cover two major use cases described
below:
+
+* Construct a test checking both output and the state. It is non-trivial to
deduce the key-value of the state from the output, and having visibility of the
state would be a huge win on testing.
+* Investigate an incident against stateful streaming query. If users observe
the incorrect output and want to track how it came up, having visibility of the
state would be required.
+
+Users can read an instance of state store, which is matched to a single
stateful operator in most cases. This means, users can expect that they can
read the entire key-value pairs in the state for a single stateful operator.
+
+Note that there could be an exception, e.g. stream-stream join, which
leverages multiple state store instances internally. The data source abstracts
the internal representation away from users and
+provides a user-friendly approach to read the state. See the section for
stream-stream join for more details.
+
+### Creating a State store for Batch Queries (all defaults)
+
+<div class="codetabs">
+
+<div data-lang="python" markdown="1">
+{% highlight python %}
+
+df = spark \
+.read \
+.format("statestore") \
+.load("<checkpointLocation>")
+
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+
+val df = spark
+.read
+.format("statestore")
+.load("<checkpointLocation>")
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+
+Dataset<Row> df = spark
+.read()
+.format("statestore")
+.load("<checkpointLocation>");
+
+{% endhighlight %}
+</div>
+
+</div>
+
+Each row in the source has the following schema:
+
+<table class="table table-striped">
+<thead><tr><th>Column</th><th>Type</th><th>Note</th></tr></thead>
+<tr>
+ <td>key</td>
+ <td>struct (depends on the type for state key)</td>
+ <td></td>
+</tr>
+<tr>
+ <td>value</td>
+ <td>struct (depends on the type for state value)</td>
+ <td></td>
+</tr>
+<tr>
+ <td>_partition_id</td>
+ <td>int</td>
+ <td>metadata column (hidden unless specified with SELECT)</td>
+</tr>
+</table>
+
+The nested columns for key and value heavily depend on the input schema of the
stateful operator as well as the type of operator.
+Users are encouraged to query about the schema via df.schema() /
df.printSchema() first to understand the type of output.
+
+The following options must be set for the source.
+
+<table class="table table-striped">
+<thead><tr><th>Option</th><th>value</th><th>meaning</th></tr></thead>
+<tr>
+ <td>path</td>
+ <td>string</td>
+ <td>Specify the root directory of the checkpoint location. You can either
specify the path via option("path", `path`) or load(`path`).</td>
+</tr>
+</table>
+
+The following configurations are optional:
+
+<table class="table table-striped">
+<thead><tr><th>Option</th><th>value</th><th>default</th><th>meaning</th></tr></thead>
+<tr>
+ <td>batchId</td>
+ <td>numeric value</td>
+ <td>latest committed batch</td>
+ <td>Represents the target batch to read from. This option is used when users
want to perform time-travel. The batch should be committed but not yet cleaned
up.</td>
+</tr>
+<tr>
+ <td>operatorId</td>
+ <td>numeric value</td>
+ <td>0</td>
+ <td>Represents the target operator to read from. This option is used when
the query is using multiple stateful operators.</td>
+</tr>
+<tr>
+ <td>storeName</td>
+ <td>string</td>
+ <td>DEFAULT</td>
+ <td>Represents the target state store name to read from. This option is used
when the stateful operator uses multiple state store instances. It is not
required except stream-stream join.</td>
+</tr>
+<tr>
+ <td>joinSide</td>
+ <td>string ("left" or "right")</td>
+ <td>(none)</td>
+ <td>Represents the target side to read from. This option is used when users
want to read the state from stream-stream join.</td>
+</tr>
+</table>
+
+### Reading state for Stream-stream join
+
+Structured Streaming implements the stream-stream join feature via leveraging
multiple instances of state store internally.
+These instances logically compose buffers to store the input rows for left and
right.
+
+Since it is more obvious to users to reason about, the data source provides
the option 'joinSide' to read the buffered input for specific side of the join.
+To enable the functionality to read the internal state store instance
directly, we also allow specifying the option 'storeName', with restriction
that 'storeName' and 'joinSide' cannot be specified together.
+
+## State metadata source
+
+Before querying the state from existing checkpoint via state data source,
users would like to understand the information for the checkpoint, especially
about state operator. This includes which operators and state store instances
are available in the checkpoint, available range of batch IDs, etc.
+
+Structured Streaming provides a data source named "State metadata source" to
provide the state-related metadata information from the checkpoint.
+
+Note: The metadata is constructed when the streaming query is running with
Spark 4.0+. The existing checkpoint which has been running with lower Spark
version does not have the metadata and will be unable to query/use with this
metadata source. It is required to run the streaming query pointing the
existing checkpoint in Spark 4.0+ to construct the metadata before querying.
+
+### Creating a State metadata store for Batch Queries
+
+<div class="codetabs">
+
+<div data-lang="python" markdown="1">
+{% highlight python %}
+
+df = spark \
+.read \
+.format("state-metadata") \
+.load("<checkpointLocation>")
+
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+
+val df = spark
+.read
+.format("state-metadata")
+.load("<checkpointLocation>")
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+
+Dataset<Row> df = spark
+.read()
+.format("state-metadata")
+.load("<checkpointLocation>");
+
+{% endhighlight %}
+</div>
+
+</div>
+
+Each row in the source has the following schema:
+
+<table class="table table-striped">
+<thead><tr><th>Column</th><th>Type</th><th>Note</th></tr></thead>
+<tr>
+ <td>operatorId</td>
+ <td>int</td>
+ <td></td>
+</tr>
+<tr>
+ <td>operatorName</td>
+ <td>string</td>
+ <td></td>
+</tr>
+<tr>
+ <td>stateStoreName</td>
+ <td>int</td>
+ <td></td>
+</tr>
+<tr>
+ <td>numPartitions</td>
+ <td>int</td>
+ <td></td>
+</tr>
+<tr>
+ <td>minBatchId</td>
+ <td>int</td>
+ <td>The minimum batch ID available for querying state. The value could be
invalid if the streaming query taking the checkpoint is running, as cleanup
would run.</td>
+</tr>
+<tr>
+ <td>maxBatchId</td>
+ <td>int</td>
+ <td>The maximum batch ID available for querying state. The value could be
invalid if the streaming query taking the checkpoint is running, as the query
will commit further batches.</td>
+</tr>
+<tr>
+ <td>_numColsPrefixKey</td>
+ <td>int</td>
+ <td>metadata column (hidden unless specified with SELECT)</td>
+</tr>
+</table>
+
+One of the major use cases of this data source is to identify the operatorId
to query if the query has multiple stateful operators, e.g. stream-stream join
followed by deduplication.
+The column 'operatorName' helps users to identify the operatorId for given
operator.
+
+Additionally, if users want to query about an internal state store instance
for a stateful operator (e.g. stream-stream join), the column 'stateStoreName'
would be useful to determine the target.
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