{"record":{"id":"68222f1bbb5ddb61","repo":"ruvnet/ruflo","slug":"batch-batchindex-failed-after-attempt-attemp","errorCode":null,"errorMessage":"Batch ${batchIndex} failed after ${attempt} attempts: ${lastError?.message}","messagePattern":"Batch (.+?) failed after (.+?) attempts: (.+?)","errorType":"exception","errorClass":null,"httpStatus":null,"severity":"error","filePath":"v3/@claude-flow/plugins/src/integrations/ruvector/streaming.ts","lineNumber":1491,"sourceCode":"      attempt++;\n      try {\n        const results = await processor(batch);\n        this.emit('batch_complete', { batchIndex, attempt, success: true });\n        return results;\n      } catch (error) {\n        lastError = error as Error;\n        this.emit('batch_error', { batchIndex, attempt, error: lastError });\n\n        if (!this.options.retryOnFailure || attempt >= this.options.maxRetries) {\n          break;\n        }\n\n        // Exponential backoff\n        await this.sleep(Math.min(1000 * Math.pow(2, attempt - 1), 10000));\n      }\n    }\n\n    throw new Error(`Batch ${batchIndex} failed after ${attempt} attempts: ${lastError?.message}`);\n  }\n\n  /**\n   * Execute a single search query.\n   */\n  private async executeSingleSearch(\n    options: VectorSearchOptions\n  ): Promise<VectorSearchResult[]> {\n    const client = await this.pool.connect();\n    try {\n      const { sql, params } = this.buildSearchQuery(options);\n      const result = await client.query<{\n        id: string | number;\n        distance: number;\n        [key: string]: unknown;\n      }>(sql, params);\n\n      const metric = options.metric ?? 'cosine';","sourceCodeStart":1473,"sourceCodeEnd":1509,"githubUrl":"https://github.com/ruvnet/ruflo/blob/fa13ee4ad60ac2090b1480656eb233521790d640/v3/@claude-flow/plugins/src/integrations/ruvector/streaming.ts#L1473-L1509","documentation":"Thrown by processSingleBatch in the ruvector streaming pipeline after the retry budget for one batch is exhausted. Each batch is attempted up to options.maxRetries with exponential backoff (1s, 2s, 4s... capped at 10s); a 'batch_error' event is emitted per failed attempt. When the processor callback keeps failing (or retryOnFailure is false, failing on the first attempt), the loop breaks and this aggregate error is thrown, wrapping the last underlying error message.","triggerScenarios":"Running streaming batch processing (e.g. streamInsert with concurrent batches) where the processor callback hits persistent DB errors: connection dropped by Postgres, malformed vector literal (dimension mismatch), unique-constraint violation with upsert off, target table missing. Also triggered deterministically when options.retryOnFailure is false and the first attempt fails.","commonSituations":"Vector dimension mismatch between embedded data and the pgvector column (e.g. 1536 vs 768 dims) so every retry fails identically; Postgres restarted or connection pool exhausted mid-ingest so all retries hit the same dead connection; maxRetries misconfigured to 1 while the database is briefly unavailable; ingesting into a table that does not exist in the configured schema.","solutions":["Read the trailing lastError message in the thrown text - it carries the real DB cause (dimension error, connection refused, undefined table); fix that root cause first","For transient outages, raise maxRetries and keep retryOnFailure true so the exponential backoff spans the outage window","Subscribe to the 'batch_error' event to log each attempt and distinguish always-failing validation errors from flaky infrastructure errors","Verify vector dimensions match the column type (e.g. vector(1536)) and that the target table/schema exists before streaming","For huge ingest jobs, use a smaller batchSize with onBatchComplete checkpoints so a single bad batch does not abort the entire stream"],"exampleFix":"// before\nconst streamer = new StreamingVectorClient(pool, {\n  batchSize: 1000,\n  maxRetries: 1, // single attempt, any transient failure throws\n});\n\n// after\nconst streamer = new StreamingVectorClient(pool, {\n  batchSize: 500,\n  maxRetries: 5,\n  retryOnFailure: true,\n});\nstreamer.on('batch_error', e =>\n  logger.warn(`batch ${e.batchIndex} attempt ${e.attempt}: ${e.error.message}`)\n);","handlingStrategy":"retry","validationCode":"// Pre-flight the ingest target so retries are only spent on transient issues\n// (e.g. confirm the pgvector column dimension matches your data before streaming)\nconst expectedDim = vectors[0]?.length;\nconst colDim = await probeColumnDimension(client, tableName);\nif (expectedDim !== undefined && colDim !== expectedDim) {\n  throw new Error(`dimension mismatch: column ${colDim} vs data ${expectedDim}`);\n}","typeGuard":null,"tryCatchPattern":"try {\n  for await (const r of streamer.streamInsert(vectors, opts)) { /* ... */ }\n} catch (err) {\n  if (err instanceof Error && /Batch \\d+ failed after \\d+ attempts/.test(err.message)) {\n    // inspect the trailing cause; checkpoint and resume from last completed batch\n    await resumeFromCheckpoint(lastCompletedBatchIndex);\n  } else {\n    throw err;\n  }\n}","preventionTips":["Verify vector dimensions and table existence before starting a streaming ingest - deterministic failures burn all retries","Set maxRetries high enough that exponential backoff (capped at 10s) spans typical DB blips","Listen to the 'batch_error' event to log each attempt and abort early on non-transient causes","Use onBatchComplete as a checkpoint so a failed batch can be resumed without re-ingesting everything"],"tags":["retry-exhausted","batch-processing","vector-database","postgresql","typescript"],"backgroundTag":"retry-limit-exceeded","analyzedSha":"fa13ee4ad60ac2090b1480656eb233521790d640","analyzedAt":"2026-08-18T21:34:22.708Z","contentChangedAt":"2026-08-18T21:34:22.708Z","schemaVersion":2},"datasetVersion":"2026-09-14T00:17:10.932Z"}