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Summary

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Highlights

• Graviton GA: We are announcing full Support for the Graviton line of CPUs for our customers in AWS for Pinot components. These SKUs are typically 15-20% cheaper.
• AZ aware Kafka consumer: Users can now configure AZ aware Kafka consumer for optimizing data transfer costs across zones. Depending on usage, this can bring about 50-75% reduction in cross AZ data transfer costs.
• Calcite Upgrade (v1.39.0): Improves MSE query performance, especially with large IN clauses.
• Alter Table Task (ATT) GA: Now production-ready, replacing SRT for efficient table updates; includes deep store sync (Alpha) and direct tiered storage uploads.
• Data Portal GA: Data Portal is now GA offering a unified interface that brings together Apache Pinot OSS tools and enterprise-grade features, making it easier than ever to manage, query, and monitor Pinot clusters — all from a single place.
• Rebalance Enhancements: Smarter defaults, risk checks, reduced data movement, and improved UI and observability.
• Snowflake Connector: Adds secure key-based authentication for ingestion.

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Key Improvements

• MSE Logging & Stats: Better error diagnostics, reduced noise, and stats on failed queries.
• Pinot Proxy Optimization: Lower CPU usage with improved broker routing.
• Segment Import Task (SIT): Fixes data loss edge cases, optimizes memory use.
• GRPC & Memory Handling: Adds throttling and auto-recovery to prevent OOM crashes.
• Faster Cleanup & Validations: Batched table deletions, better error messaging, and safer ingestion setups.

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Stability Fixes

• Fixes around Kinesis ingestion, stream partition handling, table config updates without minions, and better JSON responses for edge queries.
• Improved MSE thread management reduces contention and enhances throughput.
• Additional validation (e.g., bloom filter on boolean types) and safety checks across ingestion and query paths.

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Observability & Monitoring

• New Dashboards: For upserts, rebalances, SRT/ATT tasks, and MSE query load.
• Expanded Metrics & Alerts: More visibility into ingestion errors, ZK node size, broker failures, and query health.
• Alert Refinements: Better thresholds for CPU, GC, and system anomalies.

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StarTree Data

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New Features

• AZ aware Kafka consumer support (Beta)

"client.rack": "${CLOUD_AZ}"

• Calcite upgrade to 1.39.0

• Correlation ID for Query Tracking
  Introduced support for correlation ID, enabling better tracking and observability across multi-stage engine (MSE) queries. This helps users debug issues with MSE easily by collecting all relevant logs from brokers and servers in one place for a given query. Previously, it was difficult to track down relevant logs for a given query in question, making it harder to identify the root cause of failures / timeouts. Ability to collect all the relevant logs for a given correlation ID improves this debugging process. 

set clientQueryId='myCustomCid';
select * 
from userAttributes
limit 10

• asof join support Added support for executing asof joins in Pinot which enables user to join 2 Pinot tables based on timestamp values (specified in match condition) that follow each other, precede each other, or match exactly. For more details, please refer to this doc.
• Deep Store Sync via Alter Table Task (Alpha Release)
  Introduced a new mode in Alter Table Task (ATT) to rebuild any segments in the deep store that are out of sync with the corresponding local segments in Pinot Servers, following a table config or schema change. This runs as a background task and hence relieves the Pinot servers from doing this during a restart / upgrade operation, thus reducing overall turnaround time. Note: This background task is optional and intended in scenarios wherein we want to reduce the overhead on Pinot servers. Configure the ATT task config as given below on the table config to enable it

"task": {
  "taskTypeConfigsMap": {
    "StarTreeAlterTableTask": {
      "reloadOnly": "true",
      "forceReload": "true"
    }
  }
}

• Rebalance Phase 2 Enhancements
  • minimizeDataMovement option added to Rebalance parameters to minimize network overhead (doc). 
  • UI enhancements
    • Basic and advanced options
    • Highlight risky rebalance parameters
    • “Dry Run”, Status tracking buttons
  • New defaults: 
    • reassignInstances: true, 
    • includeConsuming: true, 
    • minAvailableReplicas: -1
  • Pre-check improvements
    • Display status of PASS / WARN / FAIL and a message
    • Check Disk utilization during and after a rebalance and flag servers that might be at risk (default threshold of 90%)
    • Flag risky & missed rebalance parameters
    • needReload pre-check now checks all servers
  • Summary improvements:
    • Always on
    • Add tag level summary
    • Add summary about the consuming segments and track the top 10 based on most offsets to catch up and oldest segment creation time
  • Observability improvements
    • Always include the jobId and table name for easier debuggability
    • New segment throttle metrics for threshold and current count (doc)
    • Improve rebalance progress stats tracking in ZK for better observability into how the rebalance is progressing
  • Update state model to prioritize DROPPED over other states, followed by OFFLINE. Modify the state model to allow direct state transitions to DROPPED from ONLINE / CONSUMING rather than the two step process that happens today (this also reduces the number of state transitions overall for DROPPED) - we enabled the rollout of this only in STP though (in OSS users will need to set a config to update the state model)
• Key Based authentication support for Snowflake connector
  Added key-pair authentication support when ingesting data from Snowflake (previously we only supported username and password). This provides a more secure way to connect and ingest data from Snowflake. Here’s a sample config to use this new feature:

{
 "sql.snowflake.account": "<Account ID>",
 "sql.snowflake.user": "<user name>",
 "sql.snowflake.privateKey": "BASE64_ENCODED_PRIVATE_KEY",
 "sql.snowflake.privateKeyPassword": "BASE64_ENCODED_PASSWORD",
 "sql.snowflake.db": "<DB name>",
 "sql.className": "ai.startree.connectors.sql.snowflake.SnowflakeConnector",
 "sql.snowflake.schema": "<schema name>"
  ...
}

• Alter Table Task (ATT) - GA
  With this release, we’re making the Alter Table Task (ATT) globally available for production. ATT will replace the older SRT (Segment Refresh Task) going ahead and is designed to be more efficient and scalable. More information on ATT can be found in this doc.
• Support for altering table config with cloud tiered storage in an efficient manner
  Users can now easily make changes to tables with cloud tiered storage (eg: add/remove indexes) in an efficient manner using SRT/ATT jobs. Previously, these jobs would generate new segments based on the specified change and upload to the deep store, which was then uploaded to the remote tier via the Pinot server. Now, this is done directly by the minion job, thus reducing server overhead.

pinot.server.instance.table.data.manager.provider.class : "ai.startree.pinot.data.tier.table.StarTreeDefaultTableDataManagerProvider"

"directUploadToTier": "true" 

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Improvements

• Indexing improvements (Text, Json) We’ve made the following improvements:
  • Text Index (Lucene): TEXT_MATCH is now extended to support Lucene query parser options at query time. For example, now we’re able to do the following
    Copy

    Ask AI

    SELECT * FROM myTable WHERE TEXT_MATCH(myCol, 'term1 term2', 'parser=STANDARD, defaultOperator=AND')
    

    For more information, please refer to the documentation.
  • Composite Json index: Introduced a composite JSON index to accelerate complex JSON queries by enabling efficient filtering and retrieval of nested fields. This now offers greater control in path selection / inclusion, configure internal range or text index. For more information, please refer to the documentation.
• Add stats on errors in MSE  The new MSE engine now shows detailed query stats even when the query fails (which was not true previously). This helps in debugging MSE query errors / timeouts and pinpoint where the failure occurred. Please see this doc for more details
• Ease of schema evolution  In this PR we’ve made it easier for users to evolve the Pinot schema on a live Pinot table. Previously, schema changes needed a reload operation to be complete before the changes are reflected correctly in the query results. Previously, in some cases users also noticed query failures during this transition period. Now, with the on the fly virtual column feature, users can safely run queries as soon as the schema change is done. The new behaviour is captured in the table below:

  Scenario	Before	After
  Rolling addition of a new column (newCol)	Queries fail with schema mismatch	Queries succeed; newCol is exposed as a virtual column
  Add a new column without segment reload	Empty results due to server‑side pruning	Queries succeed and return all columns, with newCol filled with default
  Queries over existing columns only	No change	No change
  Queries referencing a partially loaded column	Merge errors or empty responses	Queries succeed with consistent results, including the new virtual column

• Merge and compaction for Upsert enabled tables (via SegmentRefreshTask) The SegmentRefreshTask (SRT) has now been improved to support compaction and merge of segments in a real-time upsert enabled table. This included some bug fixes, along with guardrails for CPU overhead and optimizations to speed up the process. For more details, please refer to this doc.
• Reducing noisy logs in MSE on error

• Making colocated joins more efficient We can now join a left table and right table with different partitions in a colocated manner (0 data shuffle across nodes). This requires the tables to be setup in a certain way (i.e. using same partition function upstream and tables must use the same instance assignment strategy). More information on this can be found in this PR.
• Pinot Proxy CPU optimization
  Pinot Proxy allows users to send queries easily to multiple broker tenants. The proxy will determine the relevant set of brokers for the requested table(s) and route accordingly. More information on Pinot proxy can be found here. Earlier, this was adding some CPU overhead which was not ideal. 

• Always on Upsert related background tasks
  Automatic task scheduling for upsert-enabled tables with built-in support for:
  • UpsertSnapshotCreationTask
• Improved Logging for catching bad queries
  Queries are now logged as they begin execution—helping ops teams detect and cancel problematic queries in real-time before they degrade the cluster. Earlier this was done only on query completion which was insufficient.
• SegmentImportTask (SIT) Improvements
  In StarTree Cloud, users can use the SIT task to move data between realtime and offline table (hybrid mode) after a certain age. We made a bunch of improvements to SIT in this release including:
  • Data loss bug fix: There was a scenario in which data from the realtime table can be deleted (due to retention) before its moved to the offline table. This is now fixed. 
  • Segment size improvements: Improved the algorithm to create appropriately sized segments in the offline table. 
  • Minion OOM issue: Minion tasks now initialize the record readers in a lazy manner, thus preventing all the segments to be loaded at once - which in turn causes memory overhead and may lead to OOM failures.
  • Improved runbook related to SIT.

• GRPC & Trino Error Handling Enhancements
  Added support for better error feedback when segments are unavailable during GRPC or Trino-based query execution.
• Improved Error Messages for Groovy Transform Failures
  Enhanced visibility into Groovy-based transform failures during table creation flows with clearer error logging.
• Optimized Table Deletion (including deep store)
  Optimized table deletion by deleting segment files in batches (which was done sequentially before).
• Create an alert for missing upsert snapshots
  Created alert for missing upsert snapshot config. This will help prevent scenarios wherein server goes into a crash loop because the snapshots were stale and the disk wasn’t enough for the table to generate the snapshots. This is tracked using new metric:
  CLUSTER_HEALTH_TASK_UPSERT_PREBUILT_SNAPSHOT_DISABLED_TABLES_COUNT

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Bug Fixes & Stability

• Fix for Table Config Updates in the absence of minion pod
  Prevented table config update failures when no minion pod is up, by avoiding minion dependency for unrelated config changes.
• Comprehensive clean up of historical segments in deep store
  Fixed issue where .tar.gz segments created via SRT tasks were skipped by the Retention Manager.
• Better validation for FileIngestion mode
  Disallowed switching between sync and append ingestion modes post table creation to ensure state consistency.
• Direct Memory OOM Auto-Recovery
  Enhanced Pinot servers to recover from out-of-memory (OOM) situations for direct memory without needing manual pod restarts.
• Upsert Snapshot Task Monitoring
  Added failure/success tracking and alerting for snapshot creation jobs not running within 72 hours.
• Query OOM Risk Visibility
  Log queries that generate an excessive number of groups beyond a configurable threshold - as soon as it happens (instead of waiting for the query to complete). This helps in triaging failures in the presence of such expensive queries.
• Bloom filter validation
  Prevent adding bloom filter on boolean type column at the time of table creation (earlier this caused failures during segment load).
• Improved handling of stream partition unavailability
  Fixed an issue in cases when a stream partition is no longer available (reached end of life), trying to create a consuming segment for it can cause NPE. Earlier this caused ingestion from Kinesis to come to a halt when the corresponding partition/shard was unavailable.
• Fix for ingestion issues from Kinesis during resharding + pause and resume
  Fixed issue where ingestion completely stopped from Kinesis when pause/resume was invoked post resharding of Kinesis stream .
• Fix the JSON response payload for always false queries
  Always false queries eg: (select … where 1=0) now return the correct empty JSON response, something like this:

"resultTable": {
  "dataSchema": null,
  "rows": []
}

• Improved Thread Management in MSE

• Prevent server OOM due to GRPC queries

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Breaking Changes

• New server instances will now be “untagged”

• Enable logical type in Avro schema by default

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StarTree Data Portal

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Highlights

• Table Creation & Data Ingestion – Guided workflows for creating real-time and offline tables, with built-in data validation.
• Query Console – Interactive SQL editor with live data preview, table browser, and performance insights.
• Cluster Health – Real-time health dashboard on Cluster, Table, Server, Controller, Broker, Minion, etc.
• Security Manager – Fine-grained RBAC for users, groups, and service accounts.
• Integrated OSS Pinot UI – Access Cluster Manager, Zookeeper Browser, and Swagger API Explorer directly within the Data Portal.

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StarTree Cloud Platform

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New Features

• Graviton GA We’re now able to provision Graviton instances for Pinot components (Server, Broker, Controller, Minion). Graviton instances are roughly 10-20% more cost effective.
• First class support for workspaces (Private Preview)
  We’re now able to support workspaces in a given Startree Cloud environment (SaaS or BYOC). This is currently in private preview and needs a manual configuration to enable. Workspaces provide logical isolation in the same environment. In other words - users of one workspace can only see and query tables in that workspace and nothing more. This is a good mechanism in case you want to separate your data across business units or workloads.

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Improvements

• JWT Handling: Added expiry checks for JWTs. Earlier this was not honored leading to the session not being terminated post expiry.

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Dashboards & Metrics

• Grafana Updates: Added new dashboards and visual panels, including:
  • Dashboard for capturing various broker level query errors
  • New upsert/dedup dashboard
  • New dashboard for capturing rebalance related metrics
  • Segment Refresh Task (SRT), Alter Table Task (ATT) metrics dashboard
  • Estimated total server MSE query threads on broker dashboard
  • Pauseless GC dashboards
  • Improved CPU usage panels for brokers, servers, controllers
• Metrics Enhancements:
  • Added comprehensive metrics for broker-level query errors (e.g. queryErrorBrokerSegmentUnavailable, queryErrorAccessDenied, queryErrorJsonParsing, etc.), improving diagnosis and alerting.
  • Ingestion metrics
    • Rows filtered due to filter functions
    • Rows dropped due to transform or decode exceptions
  • Znode size monitoring
    • Introduced metrics and alerts for ZNode sizes under 
      • /IDEALSTATES/
      • /EXTERNALVIEW/
      • /PROPERTYSTORE/SEGMENTS/.
  • Fixed Prometheus metric name for Pinot server Netty metrics
  • Improved metrics related to Authentication performance

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Alerts & Monitoring

• New Alerts:
  • Snapshot creation failure
  • Pinot Znode size near ZK buffer limit
  • Query error code rate
• Alert Improvements: Updated CPU usage alert definitions
  • Increased duration for HighGCPercent and refined threshold