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Posted to user@spark.apache.org by Lian Jiang <ji...@gmail.com> on 2019/03/10 18:54:05 UTC
use rocksdb for spark structured streaming (SSS)
Hi,
I have a very simple SSS pipeline which does:
val query = df
.dropDuplicates(Array("Id", "receivedAt"))
.withColumn(timePartitionCol, timestamp_udfnc(col("receivedAt")))
.writeStream
.format("parquet")
.partitionBy("availabilityDomain", timePartitionCol)
.trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES))
.option("path", "/data")
.option("checkpointLocation", "/data_checkpoint")
.start()
After ingesting 2T records, the state under checkpoint folder on HDFS
(replicator factor 2) grows to 2T bytes.
My cluster has only 2T bytes which means the cluster can barely handle
further data growth.
Online spark documents
(https://docs.databricks.com/spark/latest/structured-streaming/production.html)
says using rocksdb help SSS job reduce JVM memory overhead. But I
cannot find any document how
to setup rocksdb for SSS. Spark class CheckpointReader seems to only
handle HDFS.
Any suggestions? Thanks!
Re: use rocksdb for spark structured streaming (SSS)
Posted by Lian Jiang <ji...@gmail.com>.
Thanks guys!
I am using SSS to backfill the past 3 month data. I thought I can use SSS
for both history data and new data. I just realized that SSS is not
appropriate for backfilling since the watermark relies on receivedAt which
could be 3 month ago. I will use batch job for backfill and use SSS (with
watermark and spark-states) for the real time processing.
On Sun, Mar 10, 2019 at 2:40 PM Jungtaek Lim <ka...@gmail.com> wrote:
> The query makes state growing infinitely. Could you consider watermark
> apply to "receivedAt" to let unnecessary part of state cleared out? Other
> than watermark you could implement TTL based eviction via
> flatMapGroupsWithState, though you'll need to implement your custom
> "dropDuplicate".
>
> 2019년 3월 11일 (월) 오전 5:59, Georg Heiler <ge...@gmail.com>님이 작성:
>
>> Use https://github.com/chermenin/spark-states instead
>>
>> Am So., 10. März 2019 um 20:51 Uhr schrieb Arun Mahadevan <
>> arunm@apache.org>:
>>
>>>
>>> Read the link carefully,
>>>
>>> This solution is available (*only*) in Databricks Runtime.
>>>
>>> You can enable RockDB-based state management by setting the following
>>> configuration in the SparkSession before starting the streaming query.
>>>
>>> spark.conf.set(
>>> "spark.sql.streaming.stateStore.providerClass",
>>> "com.databricks.sql.streaming.state.RocksDBStateStoreProvider")
>>>
>>>
>>> On Sun, 10 Mar 2019 at 11:54, Lian Jiang <ji...@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> I have a very simple SSS pipeline which does:
>>>>
>>>> val query = df
>>>> .dropDuplicates(Array("Id", "receivedAt"))
>>>> .withColumn(timePartitionCol, timestamp_udfnc(col("receivedAt")))
>>>> .writeStream
>>>> .format("parquet")
>>>> .partitionBy("availabilityDomain", timePartitionCol)
>>>> .trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES))
>>>> .option("path", "/data")
>>>> .option("checkpointLocation", "/data_checkpoint")
>>>> .start()
>>>>
>>>> After ingesting 2T records, the state under checkpoint folder on HDFS (replicator factor 2) grows to 2T bytes.
>>>> My cluster has only 2T bytes which means the cluster can barely handle further data growth.
>>>>
>>>> Online spark documents (https://docs.databricks.com/spark/latest/structured-streaming/production.html)
>>>> says using rocksdb help SSS job reduce JVM memory overhead. But I cannot find any document how
>>>>
>>>> to setup rocksdb for SSS. Spark class CheckpointReader seems to only handle HDFS.
>>>>
>>>> Any suggestions? Thanks!
>>>>
>>>>
>>>>
>>>>
Re: use rocksdb for spark structured streaming (SSS)
Posted by Jungtaek Lim <ka...@gmail.com>.
The query makes state growing infinitely. Could you consider watermark
apply to "receivedAt" to let unnecessary part of state cleared out? Other
than watermark you could implement TTL based eviction via
flatMapGroupsWithState, though you'll need to implement your custom
"dropDuplicate".
2019년 3월 11일 (월) 오전 5:59, Georg Heiler <ge...@gmail.com>님이 작성:
> Use https://github.com/chermenin/spark-states instead
>
> Am So., 10. März 2019 um 20:51 Uhr schrieb Arun Mahadevan <
> arunm@apache.org>:
>
>>
>> Read the link carefully,
>>
>> This solution is available (*only*) in Databricks Runtime.
>>
>> You can enable RockDB-based state management by setting the following
>> configuration in the SparkSession before starting the streaming query.
>>
>> spark.conf.set(
>> "spark.sql.streaming.stateStore.providerClass",
>> "com.databricks.sql.streaming.state.RocksDBStateStoreProvider")
>>
>>
>> On Sun, 10 Mar 2019 at 11:54, Lian Jiang <ji...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I have a very simple SSS pipeline which does:
>>>
>>> val query = df
>>> .dropDuplicates(Array("Id", "receivedAt"))
>>> .withColumn(timePartitionCol, timestamp_udfnc(col("receivedAt")))
>>> .writeStream
>>> .format("parquet")
>>> .partitionBy("availabilityDomain", timePartitionCol)
>>> .trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES))
>>> .option("path", "/data")
>>> .option("checkpointLocation", "/data_checkpoint")
>>> .start()
>>>
>>> After ingesting 2T records, the state under checkpoint folder on HDFS (replicator factor 2) grows to 2T bytes.
>>> My cluster has only 2T bytes which means the cluster can barely handle further data growth.
>>>
>>> Online spark documents (https://docs.databricks.com/spark/latest/structured-streaming/production.html)
>>> says using rocksdb help SSS job reduce JVM memory overhead. But I cannot find any document how
>>>
>>> to setup rocksdb for SSS. Spark class CheckpointReader seems to only handle HDFS.
>>>
>>> Any suggestions? Thanks!
>>>
>>>
>>>
>>>
Re: use rocksdb for spark structured streaming (SSS)
Posted by Georg Heiler <ge...@gmail.com>.
Use https://github.com/chermenin/spark-states instead
Am So., 10. März 2019 um 20:51 Uhr schrieb Arun Mahadevan <arunm@apache.org
>:
>
> Read the link carefully,
>
> This solution is available (*only*) in Databricks Runtime.
>
> You can enable RockDB-based state management by setting the following
> configuration in the SparkSession before starting the streaming query.
>
> spark.conf.set(
> "spark.sql.streaming.stateStore.providerClass",
> "com.databricks.sql.streaming.state.RocksDBStateStoreProvider")
>
>
> On Sun, 10 Mar 2019 at 11:54, Lian Jiang <ji...@gmail.com> wrote:
>
>> Hi,
>>
>> I have a very simple SSS pipeline which does:
>>
>> val query = df
>> .dropDuplicates(Array("Id", "receivedAt"))
>> .withColumn(timePartitionCol, timestamp_udfnc(col("receivedAt")))
>> .writeStream
>> .format("parquet")
>> .partitionBy("availabilityDomain", timePartitionCol)
>> .trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES))
>> .option("path", "/data")
>> .option("checkpointLocation", "/data_checkpoint")
>> .start()
>>
>> After ingesting 2T records, the state under checkpoint folder on HDFS (replicator factor 2) grows to 2T bytes.
>> My cluster has only 2T bytes which means the cluster can barely handle further data growth.
>>
>> Online spark documents (https://docs.databricks.com/spark/latest/structured-streaming/production.html)
>> says using rocksdb help SSS job reduce JVM memory overhead. But I cannot find any document how
>>
>> to setup rocksdb for SSS. Spark class CheckpointReader seems to only handle HDFS.
>>
>> Any suggestions? Thanks!
>>
>>
>>
>>
Re: use rocksdb for spark structured streaming (SSS)
Posted by Arun Mahadevan <ar...@apache.org>.
Read the link carefully,
This solution is available (*only*) in Databricks Runtime.
You can enable RockDB-based state management by setting the following
configuration in the SparkSession before starting the streaming query.
spark.conf.set(
"spark.sql.streaming.stateStore.providerClass",
"com.databricks.sql.streaming.state.RocksDBStateStoreProvider")
On Sun, 10 Mar 2019 at 11:54, Lian Jiang <ji...@gmail.com> wrote:
> Hi,
>
> I have a very simple SSS pipeline which does:
>
> val query = df
> .dropDuplicates(Array("Id", "receivedAt"))
> .withColumn(timePartitionCol, timestamp_udfnc(col("receivedAt")))
> .writeStream
> .format("parquet")
> .partitionBy("availabilityDomain", timePartitionCol)
> .trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES))
> .option("path", "/data")
> .option("checkpointLocation", "/data_checkpoint")
> .start()
>
> After ingesting 2T records, the state under checkpoint folder on HDFS (replicator factor 2) grows to 2T bytes.
> My cluster has only 2T bytes which means the cluster can barely handle further data growth.
>
> Online spark documents (https://docs.databricks.com/spark/latest/structured-streaming/production.html)
> says using rocksdb help SSS job reduce JVM memory overhead. But I cannot find any document how
>
> to setup rocksdb for SSS. Spark class CheckpointReader seems to only handle HDFS.
>
> Any suggestions? Thanks!
>
>
>
>