Presto搭配Pinot连接器查询耗时久，是否属正常性能？需如何优化？

First off, the performance you're seeing isn't ideal for TPCH queries with this setup—especially since your first query spends ~87% of its time transferring raw data from Pinot to Presto, and the second query's compute phase (joins + cube aggregation) is taking far longer than it should. Let's break down what you might be missing, and actionable steps to fix this:

1. You're Not Leveraging Pinot's Core Strengths (Big Mistake!) #

Pinot is built for fast analytics with pre-aggregated data, but you're treating it like a raw data store—pulling full datasets into Presto to do aggregations. That's wasting Pinot's biggest value:

• Precompute Rollups/Cubes in Pinot: For your first Group by Roll-up query, create a pre-rolled-up table in Pinot. This way, Pinot returns only aggregated results (not 2.3GB of raw data) to Presto, cutting transfer time from 20 seconds to milliseconds. Same logic applies to the second query's Cube aggregation—precompute the cube in Pinot if possible.
• Add Targeted Indexes: For the Lineitem table, add Sorted Index or Bitmap Index on columns used in GROUP BY, JOIN, and WHERE clauses. This lets Pinot scan only relevant data instead of full segments, reducing the volume of data sent to Presto.
• Tune Pinot's Query Configuration: Set pinot.server.query.processors to match your CPU cores (4 cores = 4-8 processors) to maximize parallelism in Pinot's query execution. Also, verify if your Pinot Server uses SSD storage—HDDs can bottleneck data reads, slowing down transfers to Presto.

2. Presto's Single Worker is a Major Compute Bottleneck #

Running a single Presto Worker (even with 8 cores) is insufficient for multi-table joins and cube aggregations. Here's how to fix this:

• Add More Presto Workers: Scale out to 2-4 Workers (each with 8C/32GB is a good starting point) to parallelize join and aggregation tasks. This will drastically cut the 95 seconds your second query spends on joins and cube calculations.
• Optimize Presto Pinot Connector Settings:
  • Enable predicate and aggregation pushdown: Set pinot.pushdown-predicates=true and pinot.pushdown-aggregations=true in your Presto connector config. This lets Pinot handle filtering and basic aggregations before sending data to Presto, reducing transfer volume.
  • Avoid broadcast joins: For large tables like Lineitem, set join-distribution-type=PARTITIONED in Presto's session settings or config to split join workloads across Workers instead of broadcasting large datasets.
• Adjust Presto Memory & Parallelism:
  • Set task.max-worker-threads to 16 (2x your CPU cores) to maximize thread utilization.
  • Tune memory limits: For your 64GB Worker, set query.max-memory-per-node=32GB and query.max-memory=64GB to prevent memory bottlenecks during joins.

3. Network & Hardware: Do You Need Upgrades? #

Before investing in new hardware, prioritize the software optimizations above—they'll deliver far more value. But if you still hit limits after that:

• Upgrade Network Bandwidth: Your first query transfers 2.3GB in 20 seconds (~1.15GB/s), which is near the limit of a 1Gbps network. Upgrading to 10Gbps between Presto and Pinot nodes will cut transfer time significantly.
• Pinot Server Hardware: If you're using HDDs, switch to SSDs to speed up segment reads. For high-throughput queries, add a second Pinot Server to split segment storage and parallelize data retrieval.
• Presto Worker CPU: If aggregations/joins are still CPU-bound after scaling Workers, upgrade to 16-core CPUs per Worker for more parallel processing power.

4. Other Overlooked Details #

• Pinot Segment Partitioning: Ensure your Lineitem table is partitioned by a relevant dimension (like l_shipdate) so queries only scan necessary segments, not the entire dataset.
• Query Optimization: For your multi-table join query, reorder joins to start with smaller tables (Nation, Region) to reduce intermediate result sizes. Use EXPLAIN in Presto to inspect the query plan and identify inefficient steps.
• Monitoring: Set up monitoring for Pinot (via Prometheus + Grafana) to track segment scan speeds, query processing time, and resource usage. For Presto, use its built-in metrics to check if Workers hit CPU/memory limits during queries.

内容的提问来源于stack exchange，提问作者ravikiran