Spark Streaming: Avoid multiple calls to DB
I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:
- Spark stream reads the Kafka offsets
- For every offset it goes to the database and maps one of values coming from the topic to another value.
- Aggregates the data
- Writes the data to the same database.
Facing this issue where after running 10-12 hours it throws too many db connection open
error. It only does it for Step 2.
Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?
Read data:
sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();
Map a value from database and aggregate data:
dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));
return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);
Writing to the database:
aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();
DAO:
public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}
public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}
java apache-spark spark-streaming spark-structured-streaming aerospike
add a comment |
I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:
- Spark stream reads the Kafka offsets
- For every offset it goes to the database and maps one of values coming from the topic to another value.
- Aggregates the data
- Writes the data to the same database.
Facing this issue where after running 10-12 hours it throws too many db connection open
error. It only does it for Step 2.
Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?
Read data:
sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();
Map a value from database and aggregate data:
dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));
return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);
Writing to the database:
aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();
DAO:
public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}
public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}
java apache-spark spark-streaming spark-structured-streaming aerospike
Isn't that because some lagging micro batches are piling up?
– user6910411
Nov 23 '18 at 17:51
add a comment |
I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:
- Spark stream reads the Kafka offsets
- For every offset it goes to the database and maps one of values coming from the topic to another value.
- Aggregates the data
- Writes the data to the same database.
Facing this issue where after running 10-12 hours it throws too many db connection open
error. It only does it for Step 2.
Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?
Read data:
sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();
Map a value from database and aggregate data:
dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));
return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);
Writing to the database:
aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();
DAO:
public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}
public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}
java apache-spark spark-streaming spark-structured-streaming aerospike
I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:
- Spark stream reads the Kafka offsets
- For every offset it goes to the database and maps one of values coming from the topic to another value.
- Aggregates the data
- Writes the data to the same database.
Facing this issue where after running 10-12 hours it throws too many db connection open
error. It only does it for Step 2.
Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?
Read data:
sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();
Map a value from database and aggregate data:
dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));
return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);
Writing to the database:
aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();
DAO:
public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}
public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}
java apache-spark spark-streaming spark-structured-streaming aerospike
java apache-spark spark-streaming spark-structured-streaming aerospike
edited Nov 23 '18 at 17:48
user6910411
34.4k1080104
34.4k1080104
asked Nov 23 '18 at 17:12
Himanshu YadavHimanshu Yadav
5,94634119223
5,94634119223
Isn't that because some lagging micro batches are piling up?
– user6910411
Nov 23 '18 at 17:51
add a comment |
Isn't that because some lagging micro batches are piling up?
– user6910411
Nov 23 '18 at 17:51
Isn't that because some lagging micro batches are piling up?
– user6910411
Nov 23 '18 at 17:51
Isn't that because some lagging micro batches are piling up?
– user6910411
Nov 23 '18 at 17:51
add a comment |
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Isn't that because some lagging micro batches are piling up?
– user6910411
Nov 23 '18 at 17:51