Throughput, batching, and efficiency of indexing pipelines.
Changelog for CocoIndex 1.0.8-1.0.16: persistent per-component state, LiveMap, rate limiting, batched target writes, BigQuery and Snowflake connectors.
CocoIndex's first post-v1 releases: stable memoization keys, scheduled live refresh, scoped stats, safer SQL connectors, and more integrations.
Featuring five new target connectors, filesystem-level change detection, Python 3.14 free-threading, and smarter pipeline lifecycle management.
Featuring batching support for CocoIndex functions, execution robustness, schema & type system improvements, custom source support, and more.
CocoIndex now batches GPU and ML workloads automatically: 5x throughput on text embeddings and AI ops, with zero configuration required.
CocoIndex updates: production readiness, scalability, and reliability, plus more customization, native integrations, and multi-modal pipeline features.
How CocoIndex's layered concurrency controls optimize data-processing performance, prevent system overload, and keep pipelines stable and efficient at scale.
What incremental processing is, who needs it, and how CocoIndex keeps an index in sync with source changes through caching, lineage tracking, and change data capture.
Handle large files in data indexing: processing granularity, fan-in/fan-out, and memory pressure, walked through a patent XML example in CocoIndex.