PolarDB for PostgreSQL (Compatible with Oracle) supports cross-node parallel execution, distributing SQL queries across multiple compute nodes. This accelerates analytical queries forPolarDB for PostgreSQL (Compatible with Oracle), maximizes PolarFileSystem I/O throughput, and improves CPU and memory utilization across all compute nodes.
Prerequisites
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For clusters created before V1.1.20 (January 2022), enable this feature manually. Go to the Quota Center, find the PolarDB PG Cross-node Parallel Execution quota, and click Request in the Operation column.
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The minor engine version of PolarDB for PostgreSQL (Compatible with Oracle) must be V1.1.11 or later. To upgrade, see Version management.
Benefits
Cross-node parallel execution offers these benefits:
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Hybrid transactional and analytical processing (HTAP):
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Run analytical queries on transactional (TP) data in real time.
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Analytical read-only nodes can be physically isolated from TP read-only nodes, preventing analytical queries from affecting TP services.
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Scales with the decoupled storage-compute architecture ofPolarDB for PostgreSQL (Compatible with Oracle):
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Add read-only nodes as needed. New nodes join the distributed parallel computing process without data resharding.
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Prevents data skew.
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Scenarios
Suited for lightweight analytical workloads in daily operations, such as account reconciliation.
Features
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Cross-node parallel execution runs analytical queries and provides HTAP capabilities.Use cross-node parallel execution for analytical queries.
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Cross-node parallel execution also accelerates index building, including B-tree and global B-tree indexes.Use cross-node parallel execution to accelerate index creation.
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BRIN index scans are supported. Set the polar_enable_px parameter to on to use BRIN indexes for improved query performance.BRIN indexes.
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Run analytical queries at the system, session, or query level.Use cross-node parallel execution for analytical queries.
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PolarDB for PostgreSQL (Compatible with Oracle) supports cross-node parallel execution on partitioned tables for improved performance.Use cross-node parallel execution on partitioned tables.