Parallel Query Processing and Optimization in Database Query Optimization

Parallel Query Processing and Optimization in Database Query Optimization

Topic Description
In modern database systems, when processing massive data, single-threaded query execution can become a performance bottleneck. Parallel query processing decomposes query tasks into multiple subtasks and leverages multi-core CPUs or distributed environments for parallel execution, significantly improving query efficiency. This topic will delve into the basic principles of parallel queries, how databases implement parallelization, how optimizers formulate parallel execution plans, and considerations in practical applications.

I. Basic Principles of Parallel Queries

  1. Objective: Reduce query response time through parallelization and fully utilize system resources (such as multi-core CPUs and I/O bandwidth).
  2. Parallel Granularity:
    • Inter-query Parallelism: Multiple independent queries execute simultaneously (e.g., in OLTP scenarios).
    • Intra-query Parallelism: A single query is split into subtasks for parallel execution (common in OLAP complex queries).
  3. Parallel Types:
    • Pipeline Parallelism: Operators (such as joins, aggregations) are connected in a pipeline, with each stage processing data simultaneously as it flows through.
    • Partition Parallelism: Data is divided into multiple partitions, each processed independently before merging results (e.g., parallel scans, hash joins).

II. How Databases Implement Parallel Queries

  1. Data Partitioning:
    • Distribute data to different nodes or threads based on key ranges (Range), hashing (Hash), or round-robin (Round-Robin).
    • Example: A table with 1 billion rows partitioned by hash, with each thread scanning its local partition to avoid contention.
  2. Task Allocation and Scheduling:
    • The main thread (coordinator) decomposes tasks into subtasks, assigns them to worker threads, and monitors progress.
    • Dynamic load balancing: If a thread has a lighter workload, more data blocks are automatically allocated to it.
  3. Result Merging:
    • After parallel scanning, result sets need to be merged (e.g., parallel sorting requires merge-sort).
    • Aggregation operations (e.g., SUM) require local aggregation followed by global summarization.

III. How the Optimizer Formulates Parallel Execution Plans

  1. Cost Model Evaluation:
    • The optimizer estimates the cost (CPU, I/O, memory overhead) of serial vs. parallel execution.
    • Considers data distribution and current system load (e.g., number of idle CPU cores).
  2. Degree of Parallelism (DOP) Selection:
    • Dynamically sets the number of parallel threads based on data volume and operation complexity.
    • Example: A full table scan on a large table may set DOP=8, while a small table query may use DOP=1 (to avoid thread overhead).
  3. Operator Parallelization Strategies:
    • Parallel Scan: Multiple threads scan different data blocks simultaneously.
    • Parallel Join:
      • Hash Join: Parallel building of hash tables and parallel probe matching.
      • Nested Loop: The outer table is partitioned, and the inner table is scanned in parallel.
    • Parallel Sort: Data is partitioned, each thread performs local sorting, and results are merged.

IV. Considerations in Practical Applications

  1. Resource Contention:
    • High parallelism may compete for CPU, memory, or I/O resources, potentially degrading performance.
    • Monitor system metrics (e.g., CPU usage, lock waits) to adjust DOP.
  2. Data Skew Issues:
    • Uneven distribution of partition keys may overload some threads (e.g., partitioning by date with one day having extremely large data).
    • Solutions: Select uniformly distributed partition keys or dynamically adjust partitioning strategies.
  3. Scenarios with Parallel Limitations:
    • Transactional update operations may require serialization (e.g., write conflicts needing locks).
    • Small queries or high-concurrency OLTP scenarios may incur additional coordination overhead from parallelization.

V. Example: Parallel Hash Join Execution Process
Assume the query: SELECT * FROM orders JOIN customers ON orders.c_id = customers.id, with data already hash-partitioned by c_id.

  1. Build Phase:
    • Each thread scans a partition of the customers table in parallel, building a local hash table.
  2. Probe Phase:
    • Each thread scans the corresponding partition of the orders table in parallel, using the local hash table for join matching.
  3. Result Merging:
    • Each thread outputs join results, and the coordinator thread merges them directly (no deduplication needed).

Summary
Parallel queries significantly enhance large-scale data processing capabilities by reasonably decomposing tasks and optimizing resource utilization. In practical applications, strategies should be dynamically adjusted based on data characteristics and system load to avoid the side effects of over-parallelization.