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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsA parallel data query splits eligible database work into tasks that can run at the same time, then combines their results. In IBM Informix, Parallel Data Query (PDQ) is the name of a specific feature; in other systems, the broader technique is usually called parallel query processing or parallel execution.
What does parallel data query mean?
In general, parallel query processing is a way to execute independent parts of one database query concurrently. The database builds an execution plan, identifies work that can be divided, assigns pieces to threads or worker nodes, and collects partial results for the final answer.
“Parallel Data Query” or “PDQ” has a narrower meaning in IBM Informix documentation: it is the name of an Informix feature for dividing complex SQL operations into subtasks and scheduling them against available server resources. The term should not be treated as the name of a universal feature shared by all databases. [IBM Informix documentation]
How does parallel query processing work?
The implementation depends on the database. A system might split data into slices and have multiple threads apply an operator to those slices, or distribute plan fragments to workers and merge the results. In a distributed framework, a coordinator may use metadata and resource information to optimize and partition a plan, then schedule its pieces across execution nodes.
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For example, openGauss describes parallelizable operators processing sliced data in multiple threads, with results summarized for the frontend. Apache Solr documents a design in which a query handler sends a plan to workers and merges their results. OGSA-DQP describes a coordinator that plans and schedules execution across nodes. These illustrate different approaches; they are not requirements every database follows. [openGauss SMP documentation] [Apache Solr SQL Query Language] [OGSA-DQP overview]
When is parallel query useful?
Parallel execution is most promising when a query has substantial independent work—such as large analytical operations—and the database has available CPU, memory, and data-source capacity. IBM describes Informix PDQ as particularly useful for complex analytical or OLAP-oriented work, rather than simple transactional operations. [IBM Informix documentation]
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It is not a guarantee of faster results. Workers need coordination, and partial results must be combined. Data movement, unevenly sized partitions, resource limits, or competing workloads can reduce or eliminate the benefit. Microsoft cautions that parallel DirectQuery operations need limits so they do not overburden the data source; its Analysis Services documentation describes a MaxParallelism property for controlling this behavior. [Microsoft Analysis Services release notes]
Parallelism within a query versus many queries at once
Parallelism inside one query means multiple workers help execute that query. Query concurrency means the database handles multiple separate queries at once. Both draw on shared resources, but they are different forms of workload: increasing workers for one query can affect other users, while allowing more simultaneous queries can affect each query’s available capacity.
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What to compare between database systems
Parallel-query behavior varies by product and version. To assess a particular system, check:
- Supported operations: which scans, joins, aggregations, or other plan operations can run in parallel.
- Data placement and movement: whether work is divided across partitions, shards, or nodes, and how intermediate results move between stages.
- Controls and resource governance: worker or thread limits, scheduling, memory budgets, and query priorities.
- Effect on other workloads: whether parallel operations can saturate the database or an external data source used by concurrent users.
There is no universal best worker count or cross-vendor speedup established by these product descriptions. Use documentation for the exact database release and test with the workload and resource limits that matter in your environment.
Quick Recap
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