[SPARK-27687][SS] Rename Kafka consumer cache capacity conf and document caching

## What changes were proposed in this pull request?

Kafka related Spark parameters has to start with `spark.kafka.` and not with `spark.sql.`. Because of this I've renamed `spark.sql.kafkaConsumerCache.capacity`.

Since Kafka consumer caching is not documented I've added this also.

## How was this patch tested?

Existing + added unit test.

```
cd docs
SKIP_API=1 jekyll build
```
and manual webpage check.

Closes #24590 from gaborgsomogyi/SPARK-27687.

Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
This commit is contained in:
Gabor Somogyi 2019-05-15 10:42:09 -07:00 committed by Dongjoon Hyun
parent d14e2d7874
commit efa303581a
4 changed files with 54 additions and 3 deletions

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@ -714,7 +714,9 @@ private[spark] object SparkConf extends Logging {
AlternateConfig("spark.yarn.kerberos.relogin.period", "3.0")),
KERBEROS_FILESYSTEMS_TO_ACCESS.key -> Seq(
AlternateConfig("spark.yarn.access.namenodes", "2.2"),
AlternateConfig("spark.yarn.access.hadoopFileSystems", "3.0"))
AlternateConfig("spark.yarn.access.hadoopFileSystems", "3.0")),
"spark.kafka.consumer.cache.capacity" -> Seq(
AlternateConfig("spark.sql.kafkaConsumerCache.capacity", "3.0"))
)
/**

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@ -416,6 +416,24 @@ The following configurations are optional:
</tr>
</table>
### Consumer Caching
It's time-consuming to initialize Kafka consumers, especially in streaming scenarios where processing time is a key factor.
Because of this, Spark caches Kafka consumers on executors. The caching key is built up from the following information:
* Topic name
* Topic partition
* Group ID
The size of the cache is limited by <code>spark.kafka.consumer.cache.capacity</code> (default: 64).
If this threshold is reached, it tries to remove the least-used entry that is currently not in use.
If it cannot be removed, then the cache will keep growing. In the worst case, the cache will grow to
the max number of concurrent tasks that can run in the executor (that is, number of tasks slots),
after which it will never reduce.
If a task fails for any reason the new task is executed with a newly created Kafka consumer for safety reasons.
At the same time the cached Kafka consumer which was used in the failed execution will be invalidated. Here it has to
be emphasized it will not be closed if any other task is using it.
## Writing Data to Kafka
Here, we describe the support for writing Streaming Queries and Batch Queries to Apache Kafka. Take note that

View file

@ -33,8 +33,9 @@ package object kafka010 { // scalastyle:ignore
.createWithDefaultString("10m")
private[kafka010] val CONSUMER_CACHE_CAPACITY =
ConfigBuilder("spark.sql.kafkaConsumerCache.capacity")
.doc("The size of consumers cached.")
ConfigBuilder("spark.kafka.consumer.cache.capacity")
.doc("The maximum number of consumers cached. Please note it's a soft limit" +
" (check Structured Streaming Kafka integration guide for further details).")
.intConf
.createWithDefault(64)
}

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@ -0,0 +1,30 @@
/*
* 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
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*
* 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,
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*/
package org.apache.spark.sql.kafka010
import org.apache.spark.{LocalSparkContext, SparkConf, SparkFunSuite}
import org.apache.spark.util.ResetSystemProperties
class KafkaSparkConfSuite extends SparkFunSuite with LocalSparkContext with ResetSystemProperties {
test("deprecated configs") {
val conf = new SparkConf()
conf.set("spark.sql.kafkaConsumerCache.capacity", "32")
assert(conf.get(CONSUMER_CACHE_CAPACITY) === 32)
}
}