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[SPARK-19584] [SS] [DOCS] update structured streaming documentation around batch mode #16918

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debe111
update structured streaming documentation around batch mode
etcondie Feb 14, 2017
081ea24
update
etcondie Feb 14, 2017
28af040
address comments from @zsxwing
etcondie Feb 14, 2017
f5318e0
updatedd @zsxwing
etcondie Feb 14, 2017
5bf32f4
update addressing comments from @zsxwing and @tdas
etcondie Feb 14, 2017
8983b8d
revised based on feedback from @tdas
etcondie Feb 14, 2017
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revision @tdas
etcondie Feb 15, 2017
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160 changes: 149 additions & 11 deletions docs/structured-streaming-kafka-integration.md
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Original file line number Diff line number Diff line change
Expand Up @@ -119,6 +119,124 @@ ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
</div>
</div>

### Creating a Kafka Source Batch
If you have a use case that is better suited to batch processing,
you can create an Dataset/DataFrame for a defined range of offsets.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}

// Subscribe to 1 topic defaults to the earliest and latest offsets
val ds1 = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()
ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]

// Subscribe to multiple topics, specifying explicit Kafka offsets
val ds2 = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""")
.option("endingOffsets", """{"topic1":{"0":50,"1":-1},"topic2":{"0":-1}}""")
.load()
ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]

// Subscribe to a pattern, at the earliest and latest offsets
val ds3 = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.load()
ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]

{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}

// Subscribe to 1 topic defaults to the earliest and latest offsets
Dataset<Row> ds1 = spark
.read()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load();
ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)");

// Subscribe to multiple topics, specifying explicit Kafka offsets
Dataset<Row> ds2 = spark
.read()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.option("startingOffsets", "{\"topic1\":{\"0\":23,\"1\":-2},\"topic2\":{\"0\":-2}}")
.option("endingOffsets", "{\"topic1\":{\"0\":50,\"1\":-1},\"topic2\":{\"0\":-1}}")
.load();
ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)");

// Subscribe to a pattern, at the earliest and latest offsets
Dataset<Row> ds3 = spark
.read()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.load();
ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)");

{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}

# Subscribe to 1 topic defaults to the earliest and latest offsets
ds1 = spark \
.read \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribe", "topic1") \
.load()
ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")

# Subscribe to multiple topics, specifying explicit Kafka offsets
ds2 = spark \
.read \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribe", "topic1,topic2") \
.option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""") \
.option("endingOffsets", """{"topic1":{"0":50,"1":-1},"topic2":{"0":-1}}""") \
.load()
ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")

# Subscribe to a pattern, at the earliest and latest offsets
ds3 = spark \
.read \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribePattern", "topic.*") \
.option("startingOffsets", "earliest") \
.option("endingOffsets", "latest") \
.load()
ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")

{% endhighlight %}
</div>
</div>

Each row in the source has the following schema:
<table class="table">
<tr><th>Column</th><th>Type</th></tr>
Expand Down Expand Up @@ -152,7 +270,8 @@ Each row in the source has the following schema:
</tr>
</table>

The following options must be set for the Kafka source.
The following options must be set for the Kafka source
for both batch and streaming queries.

<table class="table">
<tr><th>Option</th><th>value</th><th>meaning</th></tr>
Expand Down Expand Up @@ -187,50 +306,69 @@ The following options must be set for the Kafka source.
The following configurations are optional:

<table class="table">
<tr><th>Option</th><th>value</th><th>default</th><th>meaning</th></tr>
<tr><th>Option</th><th>value</th><th>default</th><th>query type</th><th>meaning</th></tr>
<tr>
<td>startingOffsets</td>
<td>earliest, latest, or json string
{"topicA":{"0":23,"1":-1},"topicB":{"0":-2}}
<td>"earliest", "latest" (streaming only), or json string
""" {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}} """
</td>
<td>latest</td>
<td>"latest" for streaming, "earliest" for batch</td>
<td>streaming and batch</td>
<td>The start point when a query is started, either "earliest" which is from the earliest offsets,
"latest" which is just from the latest offsets, or a json string specifying a starting offset for
each TopicPartition. In the json, -2 as an offset can be used to refer to earliest, -1 to latest.
Note: This only applies when a new Streaming query is started, and that resuming will always pick
up from where the query left off. Newly discovered partitions during a query will start at
Note: For batch queries, latest (either implicitly or by using -1 in json) is not allowed.
For streaming queries, this only applies when a new query is started, and that resuming will
always pick up from where the query left off. Newly discovered partitions during a query will start at
earliest.</td>
</tr>
<tr>
<td>endingOffsets</td>
<td>latest or json string
{"topicA":{"0":23,"1":-1},"topicB":{"0":-1}}
</td>
<td>latest</td>
<td>batch query</td>
<td>The end point when a batch query is ended, either "latest" which is just referred to the
latest, or a json string specifying an ending offset for each TopicPartition. In the json, -1
as an offset can be used to refer to latest, and -2 (earliest) as an offset is not allowed.</td>
</tr>
<tr>
<td>failOnDataLoss</td>
<td>true or false</td>
<td>true</td>
<td>Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or
<td>streaming query</td>
<td>Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or
offsets are out of range). This may be a false alarm. You can disable it when it doesn't work
as you expected.</td>
as you expected. Batch queries will always fail if it fails to read any data from the provided
offsets due to lost data.</td>
</tr>
<tr>
<td>kafkaConsumer.pollTimeoutMs</td>
<td>long</td>
<td>512</td>
<td>streaming and batch</td>
<td>The timeout in milliseconds to poll data from Kafka in executors.</td>
</tr>
<tr>
<td>fetchOffset.numRetries</td>
<td>int</td>
<td>3</td>
<td>Number of times to retry before giving up fatch Kafka latest offsets.</td>
<td>streaming and batch</td>
<td>Number of times to retry before giving up fetching Kafka offsets.</td>
</tr>
<tr>
<td>fetchOffset.retryIntervalMs</td>
<td>long</td>
<td>10</td>
<td>streaming and batch</td>
<td>milliseconds to wait before retrying to fetch Kafka offsets</td>
</tr>
<tr>
<td>maxOffsetsPerTrigger</td>
<td>long</td>
<td>none</td>
<td>streaming and batch</td>
<td>Rate limit on maximum number of offsets processed per trigger interval. The specified total number of offsets will be proportionally split across topicPartitions of different volume.</td>
</tr>
</table>
Expand All @@ -246,7 +384,7 @@ Note that the following Kafka params cannot be set and the Kafka source will thr
where to start instead. Structured Streaming manages which offsets are consumed internally, rather
than rely on the kafka Consumer to do it. This will ensure that no data is missed when new
topics/partitions are dynamically subscribed. Note that `startingOffsets` only applies when a new
Streaming query is started, and that resuming will always pick up from where the query left off.
streaming query is started, and that resuming will always pick up from where the query left off.
- **key.deserializer**: Keys are always deserialized as byte arrays with ByteArrayDeserializer. Use
DataFrame operations to explicitly deserialize the keys.
- **value.deserializer**: Values are always deserialized as byte arrays with ByteArrayDeserializer.
Expand Down