[
https://issues.apache.org/jira/browse/SPARK-26154?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Haripriya updated SPARK-26154:
------------------------------
Description:
Stream-stream joins using left outer join gives inconsistent output
The data processed once, is being processed again and gives null value. In
Batch 3, the input data "3" is processed. But again in batch 6, null value is
provided for same data
{code:java}
scala> import org.apache.spark.sql.functions.{col, expr}
import org.apache.spark.sql.functions.{col, expr}
scala> import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.streaming.Trigger
scala> val lines_stream1 = spark.readStream.
| format("kafka").
| option("kafka.bootstrap.servers", "ip:9092").
| option("subscribe", "topic1").
| option("includeTimestamp", true).
| load().
| selectExpr("CAST (value AS String)","CAST(timestamp AS
TIMESTAMP)").as[(String,Timestamp)].
| select(col("value") as("data"),col("timestamp") as("recordTime")).
| select("data","recordTime").
| withWatermark("recordTime", "5 seconds ")
lines_stream1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [data:
string, recordTime: timestamp]
scala> val lines_stream2 = spark.readStream.
| format("kafka").
| option("kafka.bootstrap.servers", "ip:9092").
| option("subscribe", "topic2").
| option("includeTimestamp", value = true).
| load().
| selectExpr("CAST (value AS String)","CAST(timestamp AS
TIMESTAMP)").as[(String,Timestamp)].
| select(col("value") as("data1"),col("timestamp")
as("recordTime1")).
| select("data1","recordTime1").
| withWatermark("recordTime1", "10 seconds ")
lines_stream2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [data1:
string, recordTime1: timestamp]
scala> val query = lines_stream1.join(lines_stream2, expr (
| """
| | data == data1 and
| | recordTime1 >= recordTime and
| | recordTime1 <= recordTime + interval 5 seconds
| """.stripMargin),"left").
| writeStream.
| option("truncate","false").
| outputMode("append").
| format("console").option("checkpointLocation", "/tmp/leftouter/").
| trigger(Trigger.ProcessingTime ("5 seconds")).
| start()
query: org.apache.spark.sql.streaming.StreamingQuery =
org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@1a48f55b
{code}
Step2 : Start producing data
kafka-console-producer.sh --broker-list ip:9092 --topic topic1
>1
>2
>3
>4
>5
>aa
>bb
>cc
kafka-console-producer.sh --broker-list ip:9092 --topic topic2
>2
>2
>3
>4
>5
>aa
>cc
>ee
>ee
Output obtained:
Batch: 0
-------------------------------------------
+-----+---------++----------------+
|data|recordTime|data1|recordTime1|
+-----+---------++----------------+
+-----+---------++----------------+
-------------------------------------------
Batch: 1
-------------------------------------------
+-----+---------++----------------+
|data|recordTime|data1|recordTime1|
+-----+---------++----------------+
+-----+---------++----------------+
-------------------------------------------
Batch: 2
-------------------------------------------
+-----+----------------------++----------------------------+
|data|recordTime|data1|recordTime1|
+-----+----------------------++----------------------------+
|3|2018-11-22 20:09:35.053|3|2018-11-22 20:09:36.506|
|2|2018-11-22 20:09:31.613|2|2018-11-22 20:09:33.116|
+-----+----------------------++----------------------------+
-------------------------------------------
Batch: 3
-------------------------------------------
+-----+----------------------++----------------------------+
|data|recordTime|data1|recordTime1|
+-----+----------------------++----------------------------+
|4|2018-11-22 20:09:38.654|4|2018-11-22 20:09:39.818|
+-----+----------------------++----------------------------+
-------------------------------------------
Batch: 4
-------------------------------------------
+-----+----------------------++----------------------------+
|data|recordTime|data1|recordTime1|
+-----+----------------------++----------------------------+
|5|2018-11-22 20:09:44.809|5|2018-11-22 20:09:47.452|
|1|2018-11-22 20:09:22.662|null|null|
+-----+----------------------++----------------------------+
-------------------------------------------
Batch: 5
-------------------------------------------
+-----+----------------------++----------------------------+
|data|recordTime|data1|recordTime1|
+-----+----------------------++----------------------------+
|cc|2018-11-22 20:10:06.654|cc|2018-11-22 20:10:08.701|
|aa|2018-11-22 20:10:01.536|aa|2018-11-22 20:10:03.259|
+-----+----------------------++----------------------------+
-------------------------------------------
Batch: 6
-------------------------------------------
+-----+----------------------++----------------+
|data|recordTime|data1|recordTime1|
+-----+----------------------++----------------+
|3|2018-11-22 20:09:35.053|null|null|
+-----+----------------------++----------------+
was:
Stream-stream joins using left outer join gives inconsistent output
The data processed once, is being processed again and gives null value. In
Batch 2, the input data "abc" is processed. But again in batch 5, null value
is provided for same data
Steps:
In spark-shell
scala> import org.apache.spark.sql.functions.\{col, expr}
import org.apache.spark.sql.functions.\{col, expr}
scala> import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.streaming.Trigger
scala> val lines_stream1 = spark.readStream.
| format("kafka").
| option("kafka.bootstrap.servers", "ip:9092").
| option("subscribe", "topic1").
| option("includeTimestamp", true).
| load().
| selectExpr("CAST (value AS String)","CAST(timestamp AS
TIMESTAMP)").as[(String,Timestamp)].
| select(col("value") as("data"),col("timestamp") as("recordTime")).
| select("data","recordTime").
| withWatermark("recordTime", "5 seconds ")
lines_stream1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [data:
string, recordTime: timestamp]
scala> val lines_stream2 = spark.readStream.
| format("kafka").
| option("kafka.bootstrap.servers", "ip:9092").
| option("subscribe", "topic2").
| option("includeTimestamp", value = true).
| load().
| selectExpr("CAST (value AS String)","CAST(timestamp AS
TIMESTAMP)").as[(String,Timestamp)].
| select(col("value") as("data1"),col("timestamp") as("recordTime1")).
| select("data1","recordTime1").
| withWatermark("recordTime1", "10 seconds ")
lines_stream2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [data1:
string, recordTime1: timestamp]
scala> val query = lines_stream1.join(lines_stream2, expr (
| """
| | data == data1 and
| | recordTime1 >= recordTime and
| | recordTime1 <= recordTime + interval 5 seconds
| """.stripMargin),"left").
| writeStream.
| option("truncate","false").
| outputMode("append").
| format("console").option("checkpointLocation", "/tmp/leftouter/").
| trigger(Trigger.ProcessingTime ("5 seconds")).
| start()
query: org.apache.spark.sql.streaming.StreamingQuery =
org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@1a48f55b
Step2 : Start producing data
bin/kafka-console-producer.sh --broker-list ip:9092 --topic topic1
>abc
>def
>ghi
>123
>123
>234
>345
/kafka-console-producer.sh --broker-list ip:9092 --topic topic2
>abc
>ghi
>123
>345
>234
>678
Output obtained:
-------------------------------------------
Batch: 0
-------------------------------------------
+----+----------+-----+-----------+
|data|recordTime|data1|recordTime1|
+----+----------+-----+-----------+
+----+----------+-----+-----------+
-------------------------------------------
Batch: 1
-------------------------------------------
+----+----------+-----+-----------+
|data|recordTime|data1|recordTime1|
+----+----------+-----+-----------+
+----+----------+-----+-----------+
-------------------------------------------
Batch: 2
-------------------------------------------
+----+-----------------------+-----+-----------------------+
|data|recordTime |data1|recordTime1 |
+----+-----------------------+-----+-----------------------+
|ghi |2018-11-02 10:52:26.072|ghi |2018-11-02 10:52:28.309|
|abc |2018-11-02 10:52:18.627|abc |2018-11-02 10:52:22.249|
+----+-----------------------+-----+-----------------------+
Batch: 3
-------------------------------------------
+----+-----------------------+-----+-----------------------+
|data|recordTime |data1|recordTime1 |
+----+-----------------------+-----+-----------------------+
|123 |2018-11-02 10:52:31.062|123 |2018-11-02 10:52:33.094|
+----+-----------------------+-----+-----------------------+
-------------------------------------------
Batch: 4
-------------------------------------------
+----+-----------------------+-----+-----------------------+
|data|recordTime |data1|recordTime1 |
+----+-----------------------+-----+-----------------------+
|345 |2018-11-02 10:52:41.252|345 |2018-11-02 10:52:44.178|
+----+-----------------------+-----+-----------------------+
-------------------------------------------
Batch: 5
-------------------------------------------
+----+-----------------------+-----+-----------------------+
|data|recordTime |data1|recordTime1 |
+----+-----------------------+-----+-----------------------+
|678 |2018-11-02 10:53:04.116|678 |2018-11-02 10:53:06.275|
|abc |2018-11-02 10:52:18.627|null |null |
|def |2018-11-02 10:52:24.296|null |null |
+----+-----------------------+-----+-----------------------+
> Stream-stream joins - left outer join gives inconistent output
> --------------------------------------------------------------
>
> Key: SPARK-26154
> URL: https://issues.apache.org/jira/browse/SPARK-26154
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.3.2
> Environment: Spark version - Spark 2.3.2
> OS- Suse 11
> Reporter: Haripriya
> Priority: Major
>
> Stream-stream joins using left outer join gives inconsistent output
> The data processed once, is being processed again and gives null value. In
> Batch 3, the input data "3" is processed. But again in batch 6, null value
> is provided for same data
> {code:java}
> scala> import org.apache.spark.sql.functions.{col, expr}
> import org.apache.spark.sql.functions.{col, expr}
> scala> import org.apache.spark.sql.streaming.Trigger
> import org.apache.spark.sql.streaming.Trigger
> scala> val lines_stream1 = spark.readStream.
> | format("kafka").
> | option("kafka.bootstrap.servers", "ip:9092").
> | option("subscribe", "topic1").
> | option("includeTimestamp", true).
> | load().
> | selectExpr("CAST (value AS String)","CAST(timestamp AS
> TIMESTAMP)").as[(String,Timestamp)].
> | select(col("value") as("data"),col("timestamp")
> as("recordTime")).
> | select("data","recordTime").
> | withWatermark("recordTime", "5 seconds ")
> lines_stream1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] =
> [data: string, recordTime: timestamp]
> scala> val lines_stream2 = spark.readStream.
> | format("kafka").
> | option("kafka.bootstrap.servers", "ip:9092").
> | option("subscribe", "topic2").
> | option("includeTimestamp", value = true).
> | load().
> | selectExpr("CAST (value AS String)","CAST(timestamp AS
> TIMESTAMP)").as[(String,Timestamp)].
> | select(col("value") as("data1"),col("timestamp")
> as("recordTime1")).
> | select("data1","recordTime1").
> | withWatermark("recordTime1", "10 seconds ")
> lines_stream2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] =
> [data1: string, recordTime1: timestamp]
> scala> val query = lines_stream1.join(lines_stream2, expr (
> | """
> | | data == data1 and
> | | recordTime1 >= recordTime and
> | | recordTime1 <= recordTime + interval 5 seconds
> | """.stripMargin),"left").
> | writeStream.
> | option("truncate","false").
> | outputMode("append").
> | format("console").option("checkpointLocation",
> "/tmp/leftouter/").
> | trigger(Trigger.ProcessingTime ("5 seconds")).
> | start()
> query: org.apache.spark.sql.streaming.StreamingQuery =
> org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@1a48f55b
> {code}
> Step2 : Start producing data
> kafka-console-producer.sh --broker-list ip:9092 --topic topic1
> >1
> >2
> >3
> >4
> >5
> >aa
> >bb
> >cc
> kafka-console-producer.sh --broker-list ip:9092 --topic topic2
> >2
> >2
> >3
> >4
> >5
> >aa
> >cc
> >ee
> >ee
>
> Output obtained:
> Batch: 0
> -------------------------------------------
> +-----+---------++----------------+
> |data|recordTime|data1|recordTime1|
> +-----+---------++----------------+
> +-----+---------++----------------+
> -------------------------------------------
> Batch: 1
> -------------------------------------------
> +-----+---------++----------------+
> |data|recordTime|data1|recordTime1|
> +-----+---------++----------------+
> +-----+---------++----------------+
> -------------------------------------------
> Batch: 2
> -------------------------------------------
> +-----+----------------------++----------------------------+
> |data|recordTime|data1|recordTime1|
> +-----+----------------------++----------------------------+
> |3|2018-11-22 20:09:35.053|3|2018-11-22 20:09:36.506|
> |2|2018-11-22 20:09:31.613|2|2018-11-22 20:09:33.116|
> +-----+----------------------++----------------------------+
> -------------------------------------------
> Batch: 3
> -------------------------------------------
> +-----+----------------------++----------------------------+
> |data|recordTime|data1|recordTime1|
> +-----+----------------------++----------------------------+
> |4|2018-11-22 20:09:38.654|4|2018-11-22 20:09:39.818|
> +-----+----------------------++----------------------------+
> -------------------------------------------
> Batch: 4
> -------------------------------------------
> +-----+----------------------++----------------------------+
> |data|recordTime|data1|recordTime1|
> +-----+----------------------++----------------------------+
> |5|2018-11-22 20:09:44.809|5|2018-11-22 20:09:47.452|
> |1|2018-11-22 20:09:22.662|null|null|
> +-----+----------------------++----------------------------+
> -------------------------------------------
> Batch: 5
> -------------------------------------------
> +-----+----------------------++----------------------------+
> |data|recordTime|data1|recordTime1|
> +-----+----------------------++----------------------------+
> |cc|2018-11-22 20:10:06.654|cc|2018-11-22 20:10:08.701|
> |aa|2018-11-22 20:10:01.536|aa|2018-11-22 20:10:03.259|
> +-----+----------------------++----------------------------+
> -------------------------------------------
> Batch: 6
> -------------------------------------------
> +-----+----------------------++----------------+
> |data|recordTime|data1|recordTime1|
> +-----+----------------------++----------------+
> |3|2018-11-22 20:09:35.053|null|null|
> +-----+----------------------++----------------+
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