Admission Control and Query Queuing

One way to limit resource usage through admission control is to set an upper limit on the number of concurrent queries. This is the initial technique you might use when you do not have extensive information about memory usage for your workload. The settings can be specified separately for each dynamic resource pool.

Max Running Queries

Maximum number of concurrently running queries in this pool. The default value is unlimited for CDH 5.7 or higher. (optional)

The maximum number of queries that can run concurrently in this pool. The default value is unlimited. Any queries for this pool that exceed Max Running Queries are added to the admission control queue until other queries finish. You can use Max Running Queries in the early stages of resource management, when you do not have extensive data about query memory usage, to determine if the cluster performs better overall if throttling is applied to Impala queries.

For a workload with many small queries, you typically specify a high value for this setting, or leave the default setting of "unlimited". For a workload with expensive queries, where some number of concurrent queries saturate the memory, I/O, CPU, or network capacity of the cluster, set the value low enough that the cluster resources are not overcommitted for Impala.

Once you have enabled memory-based admission control using other pool settings, you can still use Max Running Queries as a safeguard. If queries exceed either the total estimated memory or the maximum number of concurrent queries, they are added to the queue.

Max Queued Queries

Maximum number of queries that can be queued in this pool. The default value is 200 for CDH 5.3 or higher and 50 for previous versions of Impala. (optional)

Queue Timeout

The amount of time, in milliseconds, that a query waits in the admission control queue for this pool before being canceled. The default value is 60,000 milliseconds.

In the following cases, Queue Timeout is not significant, and you can specify a high value to avoid canceling queries unexpectedly:

• In a low-concurrency workload where few or no queries are queued
• In an environment without a strict SLA, where it does not matter if queries occasionally take longer than usual because they are held in admission control

You might also need to increase the value to use Impala with some business intelligence tools that have their own timeout intervals for queries.

In a high-concurrency workload, especially for queries with a tight SLA, long wait times in admission control can cause a serious problem. For example, if a query needs to run in 10 seconds, and you have tuned it so that it runs in 8 seconds, it violates its SLA if it waits in the admission control queue longer than 2 seconds. In a case like this, set a low timeout value and monitor how many queries are cancelled because of timeouts. This technique helps you to discover capacity, tuning, and scaling problems early, and helps avoid wasting resources by running expensive queries that have already missed their SLA.

If you identify some queries that can have a high timeout value, and others that benefit from a low timeout value, you can create separate pools with different values for this setting.

You can combine these settings with the memory-based approach described in Memory Limits and Admission Control. If either the maximum number of or the expected memory usage of the concurrent queries is exceeded, subsequent queries are queued until the concurrent workload falls below the threshold again.

Each dynamic resource pool can have an upper limit on the cluster-wide memory used by queries executing in that pool.

Use the following settings to manage memory-based admission control.

Max Memory

The maximum amount of aggregate memory available across the cluster to all queries executing in this pool. This should be a portion of the aggregate configured memory for Impala daemons, which will be shown in the settings dialog next to this option for convenience. Setting this to a non-zero value enables memory based admission control.

Impala determines the expected maximum memory used by all queries in the pool and holds back any further queries that would result in Max Memory being exceeded.

If you specify Max Memory, you should specify the amount of memory to allocate to each query in this pool. You can do this in two ways:

• By setting Maximum Query Memory Limit and Minimum Query Memory Limit. This is preferred in CDH 6.1 / Impala 3.1 and greater and gives Impala flexibility to set aside more memory to queries that are expected to be memory-hungry.
• By setting Default Query Memory Limit to the exact amount of memory that Impala should set aside for queries in that pool.

Note that in the following cases, Impala will rely entirely on memory estimates to determine how much memory to set aside for each query. This is not recommended because it can result in queries not running or being starved for memory if the estimates are inaccurate. And it can affect other queries running on the same node.

• Max Memory, Maximum Query Memory Limit, and Minimum Query Memory Limit are not set, and the MEM_LIMIT query option is not set for the query.
• Default Query Memory Limit is set to 0, and the MEM_LIMIT query option is not set for the query.

Minimum Query Memory Limit and Maximum Query Memory Limit

These two options determine the minimum and maximum per-host memory limit that will be chosen by Impala Admission control for queries in this resource pool. If set, Impala admission control will choose a memory limit between the minimum and maximum values based on the per-host memory estimate for the query. The memory limit chosen determines the amount of memory that Impala admission control will set aside for this query on each host that the query is running on. The aggregate memory across all of the hosts that the query is running on is counted against the pool’s Max Memory.

Minimum Query Memory Limit must be less than or equal to Maximum Query Memory Limit and Max Memory.

A user can override Impala’s choice of memory limit by setting the MEM_LIMIT query option. If the Clamp MEM_LIMIT Query Option setting is set to TRUE and the user sets MEM_LIMIT to a value that is outside of the range specified by these two options, then the effective memory limit will be either the minimum or maximum, depending on whether MEM_LIMIT is lower than or higher the range.

For example, assume a resource pool with the following parameters set:

• Minimum Query Memory Limit = 2GB
• Maximum Query Memory Limit = 10GB

If a user tries to submit a query with the MEM_LIMIT query option set to 14 GB, the following would happen:

• If Clamp MEM_LIMIT Query Option = true, admission controller would override MEM_LIMIT with 10 GB and attempt admission using that value.
• If Clamp MEM_LIMIT Query Option = false, the admission controller will retain the MEM_LIMIT of 14 GB set by the user and will attempt admission using the value.

Default Query Memory Limit

The default memory limit applied to queries executing in this pool when no explicit MEM_LIMIT query option is set. The memory limit chosen determines the amount of memory that Impala Admission control will set aside for this query on each host that the query is running on. The aggregate memory across all of the hosts that the query is running on is counted against the pool’s Max Memory. This option is deprecated in CDH 6.1 / Impala 3.1 and higher and is replaced by Maximum Query Memory Limit and Minimum Query Memory Limit.

Do not set this if either Maximum Query Memory Limit or Minimum Query Memory Limit is set.

Clamp MEM_LIMIT Query Option

If this field is not selected, the MEM_LIMIT query option will not be bounded by the Maximum Query Memory Limit and the Minimum Query Memory Limit values specified for this resource pool. By default, this field is selected in CDH 6.1 and higher. The field is disabled if both Minimum Query Memory Limit and Maximum Query Memory Limit are not set.

You can combine the memory-based settings with the upper limit on concurrent queries described in Concurrent Queries and Admission Control. If either the maximum number of or the expected memory usage of the concurrent queries is exceeded, subsequent queries are queued until the concurrent workload falls below the threshold again.

The admission control feature is similar in some ways to the Cloudera Manager static partitioning feature, as well as the YARN resource management framework. These features can be used separately or together. This section describes some similarities and differences, to help you decide which combination of resource management features to use for Impala.

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 does not interact with other Hadoop workloads such as MapReduce jobs, you might use YARN with static service pools on CDH clusters where resources are shared between Impala and other Hadoop components. This configuration is recommended when using Impala in a multitenant cluster. Devote a percentage of cluster resources to Impala, and 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 work for other components within the cluster. In this scenario, Impala's resources are not managed by YARN.

The Impala admission control feature uses the same configuration mechanism as the YARN resource manager to map users to pools and authenticate them.

Although the Impala admission control feature uses a fair-scheduler.xml configuration file behind the scenes, this file does not depend on which scheduler is used for YARN. You still use this file, and Cloudera Manager can generate it for you, even when YARN is using the capacity scheduler.

The admission control system is decentralized, embedded in each Impala daemon and communicating through the statestore mechanism. Although the limits you set for memory usage and number of concurrent queries apply cluster-wide, each Impala 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 across many coordinators. There could be times when the more queries were queued (in aggregate across the cluster) than the specified limit, or when number of admitted queries exceeds the expected number. 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 configuring memory resources, to leave some headroom in case more queries are admitted than expected, without running out of memory and being cancelled as a result.

To avoid a large backlog of queued requests, you can set an upper limit on the size of the queue for queries that are queued. When the number of queued queries exceeds this limit, further queries are cancelled rather than being queued. You can also configure a timeout period per pool, 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.

In Impala 2.0 and higher, you can submit a SQL SET statement from the client application to change the REQUEST_POOL query option. This option lets you submit queries to different resource pools, as described in REQUEST_POOL Query Option.

At any time, the set of queued queries could include queries submitted through multiple different Impala daemon hosts. All the queries submitted through a particular host will be executed in order, so a CREATE TABLE followed by an INSERT on the same table would succeed. Queries submitted through different hosts 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 hosts, set up all table structures ahead of time so that the statements controlled by the queuing 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 Impala daemon host.

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

• The other resource-related query options, RESERVATION_REQUEST_TIMEOUT and V_CPU_CORES, are no longer used. Those query options only applied to using Impala with Llama, which is no longer supported.

When queries complete quickly and are tuned for optimal memory usage, there is less chance of performance or capacity problems during times of heavy load. Before setting up admission control, tune your Impala queries to ensure that the query plans are efficient and the memory estimates are accurate. Understanding the nature of your workload, and which queries are the most resource-intensive, helps you to plan how to divide the queries into different pools and decide what limits to define for each pool.

For large tables, especially those involved in join queries, keep their statistics up to date after loading substantial amounts of new data or adding new partitions. Use the COMPUTE STATS statement for unpartitioned tables, and COMPUTE INCREMENTAL STATS for partitioned tables.

When you use dynamic resource pools with a Max Memory setting enabled, you typically override the memory estimates that Impala makes based on the statistics from the COMPUTE STATS statement. You either set the MEM_LIMIT query option within a particular session to set an upper memory limit for queries within that session, or a default MEM_LIMIT setting for all queries processed by the impalad instance, or a default MEM_LIMIT setting for all queries assigned to a particular dynamic resource pool. By designating a consistent memory limit for a set of similar queries that use the same resource pool, you avoid unnecessary query queuing or out-of-memory conditions that can arise during high-concurrency workloads when memory estimates for some queries are inaccurate.

Follow other steps from Tuning Impala for Performance to tune your queries.

The limits imposed by admission control are de-centrally managed "soft" limits. Each Impala coordinator 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 the throughput could decrease due to queries spilling to disk or contending for resources. Or queries could be cancelled if they exceed the MEM_LIMIT setting while running.

In impala-shell, you can also specify which resource pool to direct queries to by setting the REQUEST_POOL query option.

If you set up different resource pools for different users and groups, consider reusing any classifications you developed for use with Sentry security. See Enabling Sentry Authorization for Impala for details.

Where practical, use Cloudera Manager to configure the admission control parameters. The Cloudera Manager GUI is much simpler than editing the configuration files directly.

For details about all the Fair Scheduler configuration settings, see Fair Scheduler Configuration, in particular the tags such as <queue> and <aclSubmitApps> to map users and groups to particular resource pools (queues).