On a busy CDH cluster, you might find there is an optimal number of Impala queries that run concurrently. Because Impala queries are typically I/O-intensive, you might not find any throughput benefit in running more concurrent queries when the I/O capacity is fully utilized. Because Impala by default cancels queries that exceed the specified memory limit, running multiple large-scale queries at once can result in having to re-run some queries that are cancelled.

The admission control feature lets you set a cluster-wide upper limit on the number of concurrent Impala queries and on the memory used by those queries. Any additional queries are queued until the earlier ones finish, rather than being cancelled or running slowly and causing contention. As other queries finish, the queued queries are allowed to proceed.

For details on the internal workings of admission control, see How Impala Schedules and Enforces Limits on Concurrent Queries.

The admission control feature is similar in some ways to the YARN resource management framework, and they can be used separately or together. This section describes some similarities and differences, to help you decide when to use one, the other, or both together.

Admission control is a lightweight, decentralized system that is suitable for workloads consisting primarily of Impala queries and other SQL statements. It sets "soft" limits that smooth out Impala memory usage during times of heavy load, rather than taking an all-or-nothing approach that cancels jobs that are too resource-intensive.

Because the admission control system is not aware of other Hadoop workloads such as MapReduce jobs, you might use YARN with static service pools on heterogeneous CDH 5 clusters where resources are shared between Impala and other Hadoop components. Devote a percentage of cluster resources to Impala, allocate another percentage for MapReduce and other batch-style workloads; let admission control handle the concurrency and memory usage for the Impala work within the cluster, and let YARN manage the remainder of work within the cluster.

You could also try out the combination of YARN, Impala, and Llama, where YARN manages all cluster resources and Impala queries request resources from YARN by using the Llama component as an intermediary. YARN is a more centralized, general-purpose service, with somewhat higher latency than admission control due to the requirement to pass requests back and forth through the YARN and Llama components.

The Impala admission control feature uses the same mechanism as the YARN resource manager to map users to pools and authenticate them. Although the YARN resource manager is only available with CDH 5 and higher, internally Impala includes the necessary infrastructure to work consistently on both CDH 4 and CDH 5. You do not need to run the actual YARN and Llama components for admission control to operate.

In Cloudera Manager, the controls for Impala resource management change slightly depending on whether the Llama role is enabled, which brings Impala under the control of YARN. When you use Impala without the Llama role, you can specify three properties (memory limit, query queue size, and queue timeout) for the admission control feature. When the Llama role is enabled, you can specify query queue size and queue timeout, but the memory limit is enforced by YARN and not settable through the Dynamic Resource Pools page.

The admission control system is decentralized, embedded in each impalad daemon and communicating through the statestore mechanism. Although the limits you set for memory usage and number of concurrent queries apply cluster-wide, each impalad daemon makes its own decisions about whether to allow each query to run immediately or to queue it for a less-busy time. These decisions are fast, meaning the admission control mechanism is low-overhead, but might be imprecise during times of heavy load. There could be times when the query queue contained more queries than the specified limit, or when the estimated of memory usage for a query is not exact and the overall memory usage exceeds the specified limit. Thus, you typically err on the high side for the size of the queue, because there is not a big penalty for having a large number of queued queries; and you typically err on the low side for the memory limit, to leave some headroom for queries to use more memory than expected, without being cancelled as a result.

At any time, the set of queued queries could include queries submitted through multiple different impalad nodes. All the queries submitted through a particular node will be executed in order, so a CREATE TABLE followed by an INSERT on the same table would succeed. Queries submitted through different nodes are not guaranteed to be executed in the order they were received. Therefore, if you are using load-balancing or other round-robin scheduling where different statements are submitted through different nodes, set up all table structures ahead of time so that the statements controlled by the queueing system are primarily queries, where order is not significant. Or, if a sequence of statements needs to happen in strict order (such as an INSERT followed by a SELECT), submit all those statements through a single session, while connected to the same impalad node.

The limit on the number of concurrent queries is a "soft" one, To achieve high throughput, Impala makes quick decisions at the node level about which queued queries to dispatch. Therefore, Impala might slightly exceed the limit from time to time.

To avoid a large backlog of queued requests, you can also set an upper limit on the size of the queue for queries that are delayed. When the number of queued queries exceeds this limit, further queries are cancelled rather than being queued. You can also configure a timeout period, after which queued queries are cancelled, to avoid indefinite waits. If a cluster reaches this state where queries are cancelled due to too many concurrent requests or long waits for query execution to begin, that is a signal for an administrator to take action, either by provisioning more resources, scheduling work on the cluster to smooth out the load, or by doing Impala performance tuning to enable higher throughput.

Most aspects of admission control work transparently with client interfaces such as JDBC and ODBC:

• If a SQL statement is put into a queue rather than running immediately, the API call blocks until the statement is dequeued and begins execution. At that point, the client program can request to fetch results, which might also block until results become available.
• If a SQL statement is cancelled because it has been queued for too long or because it exceeded the memory limit during execution, the error is returned to the client program with a descriptive error message.

Admission control has the following limitations or special behavior when used with JDBC or ODBC applications:

• If you want to submit queries to different resource pools through the REQUEST_POOL query option, as described in REQUEST_POOL, that option is only settable for a session through the impala-shell interpreter or cluster-wide through an impalad startup option.
• The MEM_LIMIT query option, sometimes useful to work around problems caused by inaccurate memory estimates for complicated queries, is only settable through the impala-shell interpreter and cannot be used directly through JDBC or ODBC applications.
• Admission control does not use the other resource-related query options, RESERVATION_REQUEST_TIMEOUT or V_CPU_CORES. Those query options only apply to the YARN resource management framework.

The configuration options for admission control range from the simple (a single resource pool with a single set of options) to the complex (multiple resource pools with different options, each pool handling queries for a different set of users and groups). You can configure the settings through the Cloudera Manager user interface, or on a system without Cloudera Manager by editing configuration files or through startup options to the impalad daemon.

To see how admission control works for particular queries, examine the profile output for the query. This information is available through the PROFILE statement in impala-shell immediately after running a query in the shell, on the queries page of the Impala debug web UI, or in the Impala log file (basic information at log level 1, more detailed information at log level 2). The profile output contains details about the admission decision, such as whether the query was queued or not and which resource pool it was assigned to. It also includes the estimated and actual memory usage for the query, so you can fine-tune the configuration for the memory limits of the resource pools.

Where practical, use Cloudera Manager 5 to configure the admission control parameters. The Cloudera Manager GUI is much simpler than editing the configuration files directly. In Cloudera Manager 4, the admission control settings are not available directly, but you can use the impalad safety valve field to configure appropriate startup options.

Remember that the limits imposed by admission control are "soft" limits. Although the limits you specify for number of concurrent queries and amount of memory apply cluster-wide, the decentralized nature of this mechanism means that each Impala node makes its own decisions about whether to allow queries to run immediately or to queue them. These decisions rely on information passed back and forth between nodes by the statestore service. If a sudden surge in requests causes more queries than anticipated to run concurrently, then as a fallback, the overall Impala memory limit and the Linux cgroups mechanism serve as hard limits to prevent overallocation of memory, by cancelling queries if necessary.

If you have trouble getting a query to run because its estimated memory usage is too high, you can override the estimate by setting the MEM_LIMIT query option in impala-shell, then issuing the query through the shell in the same session. The MEM_LIMIT value is treated as the estimated amount of memory, overriding the estimate that Impala would generate based on table and column statistics. This value is used only for making admission control decisions, and is not pre-allocated by the query.

In impala-shell, you can also specify which resource pool to direct queries to by setting the REQUEST_POOL query option. (This option was named YARN_POOL during the CDH 5 beta period.)

The statements affected by the admission control feature are primarily queries, but also include statements that write data such as INSERT and CREATE TABLE AS SELECT. Most write operations in Impala are not resource-intensive, but inserting into a Parquet table can require substantial memory due to buffering a substantial amount of data before writing out each Parquet data block. See Loading Data into Parquet Tables for instructions about inserting data efficiently into Parquet tables.

Although admission control does not scrutinize memory usage for other kinds of DDL statements, if a query is queued due to a limit on concurrent queries or memory usage, subsequent statements in the same session are also queued so that they are processed in the correct order:

-- This query could be queued to avoid out-of-memory at times of heavy load.
select * from huge_table join enormous_table using (id);
-- If so, this subsequent statement in the same session is also queued
-- until the previous statement completes.
drop table huge_table;

If you set up different resource pools for different users and groups, consider reusing any classifications and hierarchy you developed for use with Sentry security. See Enabling Sentry Authorization for Impala for details. For details about all the Fair Scheduler configuration settings, see the Apache wiki, in particular the tags such as <queue> and <aclSubmitApps> to map users and groups to particular resource pools (queues).