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[SPARK-27687][SS] Rename Kafka consumer cache capacity conf and docum…

…ent 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>
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gaborgsomogyi authored and dongjoon-hyun committed May 15, 2019
1 parent d14e2d7 commit efa303581ac61d6f517aacd08883da2d01530bd2
@@ -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"))
)

/**
@@ -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
@@ -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)
}
@@ -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
* 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.
*/

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)
}
}

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