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

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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
tcondie Feb 14, 2017
081ea24
update
tcondie Feb 14, 2017
28af040
address comments from @zsxwing
tcondie Feb 14, 2017
f5318e0
updatedd @zsxwing
tcondie Feb 14, 2017
5bf32f4
update addressing comments from @zsxwing and @tdas
tcondie Feb 14, 2017
8983b8d
revised based on feedback from @tdas
tcondie Feb 14, 2017
d55ba09
revision @tdas
tcondie Feb 15, 2017
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150 changes: 143 additions & 7 deletions docs/structured-streaming-kafka-integration.md
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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.

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It's better to add a sentence here before the codes, such as 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
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You need to all \ to all lines.

.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 +270,7 @@ 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 (streaming and batch).
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nit: 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 +305,68 @@ 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>mode</th><th>meaning</th></tr>
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@tdas tdas Feb 14, 2017 •
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rather than "mode", how about "query type". just the term "mode" does not mean much, but "query type" says something.

<tr>
<td>startingOffsets</td>
<td>earliest, latest, or json string
<td>earliest, latest (streaming only), or json string
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can you put the possible values in quotes. e.g.

"earliest", "latest" (only for streaming) or json string """  {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}} """

{"topicA":{"0":23,"1":-1},"topicB":{"0":-2}}
</td>
<td>latest</td>
<td>streaming=latest, batch=earliest</td>
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same as above, put the value quotes. its clearer if
"latest" for streaming,
"earliest" for batch

<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, latest (either implicitly or by using -1 in json) is not allowed.
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nit: For batch queries...

For Streaming, this only applies when a new Streaming query is started, and that resuming will
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nit: For streaming queries, this only ... when a new query is stasrted

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 only</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 only</td>
<td>Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or
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nit: the streaming query
also could you add, what is the behavior for batch queries? that is the batch query will always fail if it fails to read any data.

offsets are out of range). This may be a false alarm. You can disable it when it doesn't work
as you expected.</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>streaming and batch</td>
<td>Number of times to retry before giving up fatch Kafka latest 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 Down