Performance Settings

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Overview

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Recommended settings

{
  "koalaDataExplorer.maxRows": 1000,
  "koalaDataExplorer.timeout": 120000,
  "koala.execution.fetchSize": 200,
  "koala.execution.stopOnError": true
}

• Max Rows: Applies a ROWNUM guard server-side to prevent runaway result sets.
• Timeout: Stops long-running requests that exceed your SLA.
• Fetch size: Balances memory and network calls when streaming results.

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Execution optimizations

• Query cancellation: Stops work at the tab level while other tabs keep running.
• Chunked uploads: Large SQL is compressed and chunked to reduce SOAP payload size.
• Streaming results: Fetch size tuning keeps memory flat even on wide datasets.
• Network efficiency: Gzip is applied automatically for medium/large SQL payloads.

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Benchmarks

• Compression reduces large SQL payload size by ~60% on average.
• Tab isolation keeps memory stable when multiple queries run concurrently.
• Auto column width and virtual scrolling reduce render time for wide datasets.
• Background execution keeps the editor responsive while long queries stream results.

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Before/after examples

• SQL payloads: 240 KB uncompressed → ~90 KB compressed (62% reduction).
• Multi-tab memory: 3 concurrent tabs leveled at the same memory footprint as a single tab after streaming.
• Export speed: Unlimited Excel export completes in roughly the same time as CSV when fetchSize is set between 200–500.

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Optimization tips

• Prefer targeted column lists instead of SELECT *.
• Apply WHERE filters early; ROWNUM limits are enforced even with filters present.
• Use bind variables to improve plan reuse.

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Real-world configurations

{
  "koalaDataExplorer.maxRows": 5000,           // guardrail for busy tenants
  "koalaDataExplorer.timeout": 180000,         // 3-minute ceiling for ad-hoc work
  "koala.execution.fetchSize": 300,            // balanced streaming
  "koalaDataExplorer.logging.level": "INFO"   // keep logs light in production
}