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Configure Apache Spark and Apache Hadoop in Big Data Clusters

  • Artikkeli

Important

The Microsoft SQL Server 2019 Big Data Clusters add-on will be retired. Support for SQL Server 2019 Big Data Clusters will end on February 28, 2025. All existing users of SQL Server 2019 with Software Assurance will be fully supported on the platform and the software will continue to be maintained through SQL Server cumulative updates until that time. For more information, see the announcement blog post and Big data options on the Microsoft SQL Server platform.

In order to configure Apache Spark and Apache Hadoop in Big Data Clusters, you need to modify the cluster profile at deployment time.

A Big Data Cluster has four configuration categories:

  • sql
  • hdfs
  • spark
  • gateway

sql, hdfs, spark, sql are services. Each service maps to the same named configuration category. All gateway configurations go to category gateway.

For example, all configurations in service hdfs belong to category hdfs. Note that all Hadoop (core-site), HDFS and Zookeeper configurations belong to category hdfs; all Livy, Spark, Yarn, Hive, Metastore configurations belong to category spark.

Supported configurations lists Apache Spark & Hadoop properties that you can configure when you deploy a SQL Server Big Data Cluster.

The following sections list properties that you can't modify in a cluster:

Configurations via cluster profile

In the cluster profile there are resources and services. At deployment time, we can specify configurations in one of two ways:

  • First, at the resource level:

    The following examples are the patch files for the profile:

    { 
       "op": "add", 
       "path": "spec.resources.zookeeper.spec.settings", 
       "value": { 
         "hdfs": { 
           "zoo-cfg.syncLimit": "6" 
         } 
       } 
}

    Or:

    { 
       "op": "add", 
       "path": "spec.resources.gateway.spec.settings", 
       "value": { 
         "gateway": { 
           "gateway-site.gateway.httpclient.socketTimeout": "95s" 
         } 
       } 
}

  • Second, at the service level. Assign multiple resources to a service, and specify configurations to the service.

The following is an example of the patch file for the profile for setting HDFS block size:

{ 
      "op": "add", 
      "path": "spec.services.hdfs.settings", 
      "value": { 
        "hdfs-site.dfs.block.size": "268435456" 
     } 
}

The service hdfs is defined as:

{ 
  "spec": { 
   "services": { 
     "hdfs": { 
        "resources": [ 
          "nmnode-0", 
          "zookeeper", 
          "storage-0", 
          "sparkhead" 
        ], 
        "settings":{ 
          "hdfs-site.dfs.block.size": "268435456" 
        } 
      } 
    } 
  } 
}

Note

Resource level configurations override service level configurations. One resource can be assigned to multiple services.

Enable Spark in the Storage Pool

In addition to the supported Apache configurations, we also offer the ability to configure whether or not Spark jobs can run in the Storage pool. This boolean value, includeSpark, is in the bdc.json configuration file at spec.resources.storage-0.spec.settings.spark.

An example storage pool definition in bdc.json may look like this:

...
"storage-0": {
                "metadata": {
                    "kind": "Pool",
                    "name": "default"
                },
                "spec": {
                    "type": "Storage",
                    "replicas": 2,
                    "settings": {
                        "spark": {
                            "includeSpark": "true"
                        }
                    }
                }
            }

Limitations

Configurations can only be specified at category level. To specify multiple configurations with the same sub-category, we cannot extract the common prefix in cluster profile.

{ 
      "op": "add", 
      "path": "spec.services.hdfs.settings.core-site.hadoop", 
      "value": { 
        "proxyuser.xyz.users": "*", 
        "proxyuser.abc.users": "*" 
     } 
}

Next steps