Parallelism during query execution

SQL Anywhere supports two different kinds of parallelism for query execution: inter-query, and intra-query. Inter-query parallelism involves executing different requests simultaneously on separate CPUs. Each request (task) runs on a single thread and executes on a single processor.

Intra-query parallelism involves having more than one CPU handle a single request simultaneously, so that portions of the query are computed in parallel on multi-processor hardware. Processing of these portions is handled by the Exchange algorithm (see Exchange algorithm (Exchange)). Intra-query parallelism can benefit a workload where the number of simultaneously-executing queries is usually less than the number of available processors. The maximum degree of parallelism is controlled by the setting of the max_query_tasks option (see max_query_tasks option [database]).

The optimizer estimates the extra cost of parallelism (extra copying of rows, extra costs for co-ordination of effort) and chooses parallel plans only if they are expected to improve performance.

Intra-query parallelism is not used for connections with the priority option set to background. See priority option [database].

Intra-query parallelism is not used if the number of server threads that are currently handling a request (ActiveReq server property) recently exceeded the number of CPU cores on the computer that the database server is licensed to use. The exact period of time is decided by the server and is normally a few seconds. See Server-level properties.

Parallel execution

Whether a query can take advantage of parallel execution depends on a variety of factors:

  • the available resources in the system at the time of optimization (such as memory, amount of data in cache, and so on)
  • the number of logical processors on the computer
  • the number of disk devices used for the storage of the database, and their speed relative to that of the processor and the computer's I/O architecture.
  • the specific algebraic operators required by the request. SQL Anywhere supports five algebraic operators that can execute in parallel:
    • parallel sequential scan (table scan)
    • parallel index scan
    • parallel hash join, and parallel versions of hash semijoin and anti-semijoin
    • parallel nested loop joins, and parallel versions of nested loop semijoin and anti-semijoin
    • parallel hash filter
    • parallel hash group by

A query that uses unsupported operators can still execute in parallel in some cases, but the supported operators must appear below the unsupported ones in the plan (as viewed in dbisql). A query where most of the unsupported operators can appear near the top is more likely to use parallelism. For example, a sort operator cannot be parallelized but a query that uses an ORDER BY on the outermost block may be parallelized by positioning the sort at the top of the plan and all of the parallel operators below it. In contrast, a query that uses a TOP n and ORDER BY in a derived table is less likely to use parallelism since the sort must appear somewhere other than the top of the plan.

By default, SQL Anywhere assumes that any dbspace resides on a disk subsystem with a single platter. While there can be advantages to parallel query execution in such an environment, the optimizer's I/O cost model for a single device makes it difficult for the optimizer to choose a parallel table or index scan unless the table data is fully resident in the cache. However, by calibrating the I/O subsystem using the ALTER DATABASE CALIBRATE PARALLEL READ statement, the optimizer can then cost more accurately the benefits of parallel execution, and in the case of multiple spindles, the optimizer is much more likely to choose execution plans with some degree of parallelism.

When intra-query parallelism is used for an access plan, the plan contains an Exchange operator whose effect is to merge (union) the results of the parallel computation of each subtree. The number of subtrees underneath the Exchange operator is the degree of parallelism. Each subtree, or access plan component, is a database server task (see -gn server option). The database server kernel schedules these tasks for execution in the same manner as if they were individual SQL requests, based on the availability of execution threads (or fibers). This architecture means that parallel computation of any access plan is largely self-tuning, in that work for a parallel execution task is scheduled on a thread (fiber) as the server kernel allows, and execution of the plan components is performed evenly.

See also

Circumstances under which parallelism is used

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