$$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works resource allocation, resource isolation, and sharing. On the other hand, we need to enable Spark’s DRA feature for the engines’ executors’ elastic scaling. The Basics of Dynamic Resource Allocation Spark provides to the cluster if they are no longer used and request them again later when there is demand. This feature is handy if multiple applications share resources on YARN, Kubernetes, and other platforms. For

$$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works resource allocation, resource isolation, and sharing. On the other hand, we need to enable Spark’s DRA feature for the engines’ executors’ elastic scaling. The Basics of Dynamic Resource Allocation Spark provides to the cluster if they are no longer used and request them again later when there is demand. This feature is handy if multiple applications share resources on YARN, Kubernetes, and other platforms. For

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works resource allocation, resource isolation, and sharing. On the other hand, we need to enable Spark’s DRA feature for the engines’ executors’ elastic scaling. 66 Chapter 6. High Availability Kyuubi, Release 1 to the cluster if they are no longer used and request them again later when there is demand. This feature is handy if multiple applications share resources on YARN, Kubernetes, and other platforms. For

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works resource allocation, resource isolation, and sharing. On the other hand, we need to enable Spark’s DRA feature for the engines’ executors’ elastic scaling. 66 Chapter 6. High Availability Kyuubi, Release 1 to the cluster if they are no longer used and request them again later when there is demand. This feature is handy if multiple applications share resources on YARN, Kubernetes, and other platforms. For

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works peers if could. 38 Chapter 3. What’s Next Kyuubi, Release 1.9.0-SNAPSHOT Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is of a single application is predictable. • Cars have various shapes to meet our needs. With this feature, Kyuubi give you a more flexible way to handle different big data workloads. The current supported

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works words, an engine is cluster widely shared by all Kyuubi server peers if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is of a single application is predictable. • Cars have various shapes to meet our needs. With this feature, Kyuubi give you a more flexible way to handle different big data workloads. The current supported

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works words, an engine is cluster widely shared by all Kyuubi server peers if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is of a single application is predictable. • Cars have various shapes to meet our needs. With this feature, Kyuubi give you a more flexible way to handle different big data workloads. The current supported

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works words, an engine is cluster widely shared by all Kyuubi server peers if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is of a single application is predictable. • Cars have various shapes to meet our needs. With this feature, Kyuubi give you a more flexible way to handle different big data workloads. The current supported

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works words, an engine is cluster widely shared by all Kyuubi server peers if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is of a single application is predictable. • Cars have various shapes to meet our needs. With this feature, Kyuubi give you a more flexible way to handle different big data workloads. The current supported

$SPARK_HOME/conf/hive-site.xml and $KYUUBI_HOME/conf/ kyuubi-defaults.conf for each user account. With this feature, end users are possible to visit different Hive metastore server instance. Similarly, this works words, an engine is cluster widely shared by all Kyuubi server peers if could. Why do we need this feature? Apache Spark is a unified engine for large-scale data analytics. Using Spark to process data is of a single application is predictable. • Cars have various shapes to meet our needs. With this feature, Kyuubi give you a more flexible way to handle different big data workloads. The current supported