Live Long And Process (LLAP) functionality was added in Hive 2.0 ( and associated tasks). Links documentation, features, and issues for this enhancement. For configuration of LLAP, see the LLAP Section of.

Overview Hive has become significantly faster thanks to various features and improvements that were built by the community in recent years, including. The following were needed to take Hive to the next level: • Asynchronous spindle-aware IO • Pre-fetching and caching of column chunks • Multi-threaded JIT-friendly operator pipelines Also known as Live Long and Process, LLAP provides a hybrid execution model. It consists of a long-lived daemon which replaces direct interactions with the HDFS DataNode, and a tightly integrated DAG-based framework.

Functionality such as caching, pre-fetching, some query processing and access control are moved into the daemon. Small/short queries are largely processed by this daemon directly, while any heavy lifting will be performed in standard YARN containers. Similar to the DataNode, LLAP daemons can be used by other applications as well, especially if a relational view on the data is preferred over file-centric processing. The daemon is also open through optional APIs (e.g., InputFormat) that can be leveraged by other data processing frameworks as a building block. Last, but not least, fine-grained column-level access control – a key requirement for mainstream adoption of Hive – fits nicely into this model. The diagram below shows an example execution with LLAP.

Tez AM orchestrates overall execution. The initial stage of the query is pushed into LLAP. In the reduce stage, large shuffles are performed in separate containers. Multiple queries and applications can access LLAP concurrently. Persistent Daemon To facilitate caching and JIT optimization, and to eliminate most of the startup costs, a daemon runs on the worker nodes on the cluster. The daemon handles I/O, caching, and query fragment execution.

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• These nodes are stateless. Any request to an LLAP node contains the data location and metadata. It processes local and remote locations; locality is the caller’s responsibility (YARN). • Recovery/resiliency. Failure and recovery is simplified because any data node can still be used to process any fragment of the input data.

The Tez AM can thus simply rerun failed fragments on the cluster. • Communication between nodes. LLAP nodes are able to share data (e.g., fetching partitions, broadcasting fragments). This is realized with the same mechanisms used in Tez. Execution Engine LLAP works within existing, process-based Hive execution to preserve the scalability and versatility of Hive. It does not replace the existing execution model but rather enhances it.

Sportbike sprint game online. • The daemons are optional. Hive can work without them and also is able to bypass them even if they are deployed and operational. Feature parity with regard to language features is maintained. • External orchestration and execution engines.

LLAP is not an execution engine (like MapReduce or Tez). Overall execution is scheduled and monitored by an existing Hive execution engine (such as Tez) transparently over both LLAP nodes, as well as regular containers. Obviously, LLAP level of support depends on each individual execution engine (starting with Tez).

MapReduce support is not planned, but other engines may be added later. Other frameworks like Pig also have the choice of using LLAP daemons.

• Partial execution. The result of the work performed by an LLAP daemon can either form part of the result of a Hive query, or be passed on to external Hive tasks, depending on the query.

• Resource Management. YARN remains responsible for the management and allocation of resources. The model is used to allow the transfer of allocated resources to LLAP. To avoid the limitations of JVM memory settings, cached data is kept off-heap, as well as large buffers for processing (e.g., group by, joins). This way, the daemon can use a small amount of memory, and additional resources (i.e., CPU and memory) will be assigned based on workload. Query Fragment Execution For partial execution as described above, LLAP nodes execute “query fragments” such as filters, projections, data transformations, partial aggregates, sorting, bucketing, hash joins/semi-joins, etc.