HPE Ezmeral Data Fabric Event Data Streams Java Applications

This section contains information on developing client applications with Java including information about the HPE Ezmeral Data Fabric Event Data Streams and Apache Kafka Java APIs, configuration parameters, and compiling and running producers and consumers.

Apache Kafka Support

HPE Ezmeral Data Fabric supports the following Apache Kafka Java API versions:
Table 1. Supported Apache Kafka APIs
Core version Apache Kafka API
As of 6.2 2.1
As of 6.1 1.1
As of 6.0.1 1.0
6.0.0 and earlier 0.9.0

Log Compaction

As of HPE Ezmeral Data Fabric 6.1, log compaction is supported. Log compaction can be enabled for streams created with HPE Ezmeral Data Fabric core 6.1 and later. In addition, clients older thanHPE Ezmeral Data Fabric 6.1 are prevented from consuming from streams that have had log compaction enabled on them at least once in their lifetime.

When a stream on a source cluster has both log compaction and replication enabled, the replica cluster does not automatically have log compaction enabled. You must explicitly enable log compaction on the replica cluster.
  • If a replica cluster has been upgraded and the stream data for a source cluster is compacted (that is, one or more messages have been deleted), then the source cluster replicates the compacted data to the replica cluster.
  • If a replica cluster has not been upgraded, then the source cluster fails the replication and an error is generated that requests an replica cluster upgrade.

In the context of a scan by a client that is not upgraded, the (upgraded) server inspects the row header to check if it is serving a compacted row. If it is serving a compacted row, then the server fails the consumer request. This behavior applies both to a stream that is explicitly configured for compaction and a replica that has received a compacted row.

Important: To perform log compaction on older streams, the -force option can be used. The -force option should only be used when ALL clients have been upgraded to HPE Ezmeral Data Fabric 6.1.

Idempotent Producer

As of HPE Ezmeral Data Fabric 6.1, the idempotent producer (exactly once) feature is supported. You can implement the idempotent producer with HPE Ezmeral Data Fabric core 6.1 and later.

When creating a producer instance, use the following configuration:
props.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, true)

The idempotent producer feature is supported by MEP HPE Ezmeral Data Fabric 6.0 clients and HPE Ezmeral Data Fabric 6.1.0 servers.

  • You must upgrade all servers to v6.1.0 and enable all the v6.1.0 features, before you enable the idempotent producer.
  • If you use a pre-HPE Ezmeral Data Fabric 6.1 client and a HPE Ezmeral Data Fabric 6.1 server, and if a group of messages are atomically persisted without a valid producer ID, the server treats the request as a non-idempotent producer.
  • If you use a HPE Ezmeral Data Fabric 6.1 client and a pre-HPE Ezmeral Data Fabric 6.1 server, the idempotent producer is not supported. In this case, the idempotent producer fails to produce to the stream and the following exception is thrown:
    Exception in thread "main" java.util.concurrent.ExecutionException: org.apache.kafka.common.errors.UnknownTopicOrPartitionException: Operation not permitted (1) null
        at com.mapr.streams.impl.producer.MarlinFuture.valueOrError(MarlinFuture.java:46)
        at com.mapr.streams.impl.producer.MarlinFuture.get(MarlinFuture.java:41)
        at com.mapr.streams.impl.producer.MarlinFuture.get(MarlinFuture.java:17)
        at com.mapr.qa.marlin.common.StandaloneProducer.main(StandaloneProducer.java:75)
Caused by: org.apache.kafka.common.errors.UnknownTopicOrPartitionException: Operation not permitted (1) null

TimestampType Permissions

The following discussion describes the Access Control Expression (ACE) permissions that you need when using the timestamp type parameter. See Stream Security for general information about HPE Ezmeral Data Fabric Event Data Streams streams security.

A HPE Ezmeral Data Fabric Event Data Streams stream topic inherits the default timestamp type value from its stream. To override the stream's default value, set the timestamp type for the topic to a different value.

  • Setting the value at the stream-level requires adminperm permissions. The stream-level timestamp type parameter is defaulttimestamptype. See stream create and stream edit for more information on setting this parameter using the maprcli command.
  • Setting the timestamptype at the topic-level requires topicperm permissions. The topic-level timestamp type parameter is timestamptype. See stream topic create and stream topic edit for more information on setting this parameter using the maprcli command.

User Impersonation

As of HPE Ezmeral Data Fabric 6.0, user impersonation is supported for HPE Ezmeral Data Fabric Event Data Streams.

You can set up user impersonation programmatically. To do so, use the UserGroupInformation.doAs() method in the Hadoop documentation. See Class UserGroupInformation for more information.

If you are setting up user impersonation in a secure cluster, you need to generate an impersonation ticket. See the Generating and Printing Service with Impersonation Ticket section in the maprlogin Command Examples topic.

After generating the ticket:
  1. Ensure that user mapruser1 has read permissions on the ticket.
  2. If you moved the ticket file to a different location, set the $MAPR_TICKETFILE_LOCATION environment variable with the appropriate path.
For more information about impersonation, see:

Backward Compatibility

As of HPE Ezmeral Data Fabric 6.0.1, along with the support of Apache Kafka, the java.util.Collection interface is being used. This impacts applications using certain APIs. See HPE Ezmeral Data Fabric Event Data Streams Java API Library for detailed information.

References