# Ray 2.49.0 — Ray-2.49.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-08-26
- Version: 2.49.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.49.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.49.0

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

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- **added** — Wrapped batch indices in a BatchMetadata object to make per-batch metadata explicit
- **added** — Added support for Anti/Semi Join types
- **added** — Introduced an Issue Detection Framework
- **added** — Added an option to enable out-of-order execution for better performance
- **added** — Introduced a StreamingSplit logical operator for DAG rewrite
- **added** — Added a stratify parameter to train_test_split
- **added** — Added Snowflake connectors
- **added** — Updated Hudi integration to support incremental query
- **added** — Added an Actor location tracker
- **added** — Added BundleQueue.has_next
- **added** — Made DEFAULT_OBJECT_STORE_MEMORY_LIMIT_FRACTION configurable
- **added** — Added Expression support and a with_columns API
- **added** — Allocate GPU resources in ResourceManager
- **added** — Introduced JaxTrainer with SPMD support on TPUs
- **changed** — Decoupled actor and node autoscaling; autoscaling now also considers budget
- **changed** — Faster hash-shuffle resource usage calculation and more accurate shuffle progress totals
- **changed** — Reduced Parquet metadata storage usage
- **changed** — map_batches now defaults to row_modification=False and avoids pushing past limit
- **changed** — ray.train.get_dataset_shard now lazily configures dataset sharding for better startup behavior
- **fixed** — AutoscalingActorPool now properly downscales after execution

#### Release Highlights
**Ray Data:** 

- We’ve implemented a variety of performance enhancements, including improved actor/node autoscaling with budget-aware decisions; faster/more accurate shuffle accounting; reduced Parquet metadata footprint; and out-of-order execution for higher throughput.
- We’ve also implemented anti/semi joins, stratified train_test_split, and added Snowflake connectors.

**Ray Core:** 

- Performance/robustness cleanups around GCS publish path and raylet internals; simpler OpenTelemetry flagging; new user-facing API to wait for GPU tensor free; plus assorted test/infra tidy-ups

**Ray Train:**

- We’ve introduced a new JaxTrainer with SPMD support for TPUs.

**Ray Serve:**

- Custom Autoscaling per Deployment Serve now supports user-defined autoscaling policies via AutoscalingContext and AutoscalingPolicy, enabling fine-grained scaling logic at the deployment level. This is part of a large effort where we are adding support for autoscaling based on custom metrics in Serve, see [this RFC](https://github.com/ray-project/ray/issues/41135#issuecomment-3156717488) for more details.
- Async Inference (Initial Support): Ray Serve introduces asynchronous inference execution, laying the foundation for better throughput and latency in async workloads. Please see [this RFC](https://github.com/ray-project/ray/issues/54652) for more details.
- Major Performance Gains: This version of ray serve brings double digit % performance improvements both in throughput and latency. See release notes for more details.

**Ray Serve/Data LLM:**

- We’ve refactored Ray Serve LLM to be fully compatible with the default vllm serve and also now supports vLLM=0.10.
- We’ve added a prefix cache-aware router with PrefixCacheAffinityRouter for optimized cache utilization; dynamic cache management via reset prefix cache remote methods; enhanced LMCacheConnectorV1 with kv_transfer_config support.

#### Ray Libraries
##### Ray Data
🎉 New Features:

- Wrapped batch indices in a BatchMetadata object to make per-batch metadata explicit. (#55643)
- Added support for Anti/Semi Join types. (#55272)
- Introduced an Issue Detection Framework. (#55155)
- Added an option to enable out-of-order execution for better performance. (#54504)
- Introduced a StreamingSplit logical operator for DAG rewrite. (#54994)
- Added a stratify parameter to train_test_split. (#54624)
- Added Snowflake connectors. (#51429)
- Updated Hudi integration to support incremental query. (#54301)
- Added an Actor location tracker. (#54590)
- Added BundleQueue.has_next. (#54710)
- Made DEFAULT_OBJECT_STORE_MEMORY_LIMIT_FRACTION configurable. (#54873)
- Added Expression support & a with_columns API. (#54322)
- Allocate GPU resources in ResourceManager. (#54445)

💫 Enhancements:

- Decoupled actor and node autoscaling; autoscaling now also considers budget. (#55673, #54902)
- Faster hash-shuffle resource usage calculation; more accurate shuffle progress totals. (#55503, #55543)
- Reduced Parquet metadata storage usage. (#54821)
- Export API improvements: refresh dataset/operator state, sanitize metadata, and truncate exported metadata. (#55355, #55379, #55216, #54623)
- Metrics & observability: task metric improvements, external-buffer block-count metric, row-based metrics, clearer operator names in logs, single debug log when aggregators are ready. (#55429, #55022, #54693, #52949, #54483)
- Dashboard: added “Max Bytes to Read” panel/budget, panels for blocks-per-task and bytes-per-block, and streaming executor duration. (#55024, #55020, #54614)
- Planner/execution & infra cleanups: ExecutionResources and StatsManager cleanup, planner interface refactor, node trackers init, removed ray.get in _MapWorker ctor, removed target_shuffle_max_block_size. (#54694, #55400, #55018, #54665, #54734, #55158)
- Behavior/interop tweaks: map_batches defaults to row_modification=False and avoids pushing past limit; limited operator pushdown;

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-2.49.0]_