At Yunqi 2026, Alibaba Cloud unveiled its Agentic Lake + Agentic Streaming architecture, combining DLF, Flink, EMR Ray/Daft, StarRocks, and Milvus to provide a unified multimodal data infrastructure for AI agents in autonomous driving, embodied intelligence, and enterprise operations.
This article details production practices for accelerating StarRocks queries on Paimon external tables, covering architecture, table design (partitioning, bucketing, compaction), dirty data handling, catalog configuration, predicate pushdown verification, SQL optimization with partition/bucket pruning, query method selection (direct, async materialized views, hot/cold tiering), and BE-level tuning.
This article explains how StarRocks queries Paimon Variant data, covering the differences between regular Parquet JSON and Variant, the three-phase read architecture, Shredding optimization for hot paths, practical SQL examples using get_variant_* functions, and a decision framework for choosing between JSON, Plain Variant, Shredded Variant, and formal typed columns.
This article explains why syntactically correct AI-generated SQL often fails to reflect business logic, introduces StarRocks' Semantic View as an executable 'information contract' that defines metrics, relationships, and synonyms, and demonstrates with a fishing gear example how it enables deterministic SQL expansion for both AI agents and BI tools.
This article analyzes why identical StarRocks queries on Paimon external tables run fast in early morning but slow dramatically during daytime peaks, identifying five layered root causes from file fragmentation to storage pressure, and provides a systematic troubleshooting methodology plus a four-layer optimization framework covering source governance, compute caching, storage scaling, and business scheduling.
China's high-quality dataset count hit 120,000 totaling 1,565 PB with 60% quarterly growth, exposing four systemic gaps in traditional BI-oriented lakehouse architectures — storage, governance, compute performance, and security — and driving an AI-native reference architecture built on Paimon, StarRocks, tiered storage, operator-level lineage, and multi-modal retrieval.
This article deeply compares two StarRocks real-time wide table architectures—direct primary key table writes versus Paimon lakehouse external tables—across seven dimensions including query performance, write latency, data reuse, resource isolation, operational complexity, storage cost, and evolution potential, providing banking scenario selection criteria and a recommended Flink+Fluss+Paimon+StarRocks streaming lakehouse architecture.
The article analyzes how StarRocks, Fluss, and Paimon can replace the traditional Lambda architecture with a unified lakehouse solution that delivers second‑level data freshness, consolidates storage and compute, and provides native read/write paths for high‑performance real‑time analytics and large‑scale ETL.
This guide compares Doris, ClickHouse, and StarRocks for billion‑row real‑time analytics, outlining their strengths and weaknesses across write throughput, update handling, join performance, concurrency, cost, and operational complexity, and provides concrete modeling, ingestion, query governance, and fault‑tolerance recommendations to help teams choose the most suitable OLAP platform.
The DSP advertising data pipeline was re‑architected by splitting three distinct links—NRR, BT, and CT—and assigning DLF Paimon for massive low‑frequency data, EMR Serverless StarRocks primary tables for high‑value low‑latency queries, and unified service‑layer merging, achieving 2‑minute freshness, sub‑5 ms point queries, ~60% storage cost reduction, and improved fault isolation.
Alibaba Cloud's EMR Serverless StarRocks Stella 2.2 unifies vector search, full-text search, and AI functions across both internal and lake tables (Paimon, Iceberg, Lance), enabling a single SQL interface for multimodal retrieval, AI-powered analytics, and RAG workloads without data movement.
The article analyzes the newly released AI Function suite in Alibaba Cloud EMR Serverless StarRocks, detailing multimodal embedding, AI‑driven aggregation, semantic filtering, mixed vector‑full‑text search, architectural advantages such as SQL‑native execution, async pipelines, bounded resources, and real‑world use cases in advertising, gaming, and finance.
The article details how Taotian Group’s senior data engineer Zhu Ao designed a lake‑stream integrated architecture using Fluss for second‑level real‑time storage, Paimon for minute‑level lakehouse persistence, and StarRocks as a unified OLAP query layer, achieving over 50% faster development and more than 80% cost reduction.
The talk presents a three‑layer architecture for enterprise Data Agents, explains how StarRocks‑based AI‑native real‑time data foundations, Semantic View, and context layers work together, and outlines practical checks and feedback loops to address metric, permission, latency, and governance bottlenecks.
StarRocks 4.1 introduces an incremental materialized view for Apache Iceberg that ties refresh cost to data changes instead of table size, dramatically cutting refresh time, maintaining low latency, and keeping query results fresh even as tables scale to terabytes or petabytes, with a fallback to partition refresh when needed.
StarRocks 4.1 introduces automatic multi‑tenant data management, large‑capacity tablets, second‑level schema evolution, enhanced cache observability, and deeper Iceberg support, addressing static data distribution, data skew, high repair costs and expertise requirements while delivering up to 1.86× higher throughput and dramatically lower latency in production workloads.
This article presents a lake‑stream integrated solution that combines Apache Fluss, Apache Paimon, and StarRocks to achieve second‑level data freshness, tenfold storage cost reduction, and a single‑query access pattern for both real‑time and historical data, detailing its architecture, advantages, query modes, and future roadmap.
This article details Fresha's real‑world integration of StarRocks with Apache Iceberg, covering metadata planning, distributed execution, adaptive metadata retrieval, hot‑cold data layering, missing statistics handling, catalog configuration, and performance optimizations that together demonstrate how federated queries can be efficiently executed over data‑lake tables.
This comprehensive guide covers Bitmap index internals, cardinality thresholds, index replacement rules, and layered optimization combining partition pruning, BloomFilter, and Range-Bitmap for StarRocks querying Paimon tables, with official binary formats, production thresholds, and real-world case studies.
Fresha replaced its Postgres‑Snowflake‑MSK pipeline with an AutoMQ‑based Diskless Kafka message layer and StarRocks for real‑time analytics, cutting storage costs 17‑20×, dropping query latency from seconds to sub‑second, and migrating ~1,000 topics in a week with zero downtime.
This article analyzes Paimon Bloom Index internals, demonstrates a real-world 25x query acceleration (38s to 1.5s) on a 400GB partitioned table, explains false positive rates, optimal use cases, and provides production-ready configuration patterns for StarRocks+Paimon lakehouse architectures.
This article demonstrates how to leverage StarRocks AI Function on EMR Serverless to perform sentiment analysis, intelligent classification, information extraction, and PII redaction on financial text entirely within SQL, eliminating data export, reducing latency, and ensuring compliance while providing concrete code examples, performance benchmarks, and best‑practice recommendations.
The article introduces starrocks‑debug‑skills, an open‑source, three‑layer knowledge base (Skills, Cases, Tools) that captures real‑world StarRocks troubleshooting experience, shows how AI assistants can use it to diagnose issues such as import timeouts, version errors, and compaction slowdowns, and explains how to contribute new cases.
The Streaming Lakehouse Meetup (May 27) explores breaking data silos by unifying structured tables, images, video, audio, and high‑dimensional vectors through StarRocks‑Paimon‑Fluss integration, covering multimodal fusion retrieval, vector search internals, native reader/writer performance gains, and real‑world ANN indexing practices.
KaptureCX migrated its core analytics from ClickHouse to StarRocks, introduced RisingWave and Kafka for CDC, and achieved millisecond‑level query latency, a reporting cycle cut from weeks to one day, and a solid data foundation for AI‑driven services.
Traditional databases were built for low‑frequency, human‑driven queries, but AI agents generate dozens of concurrent, sub‑second queries that expose architectural limits, and StarRocks addresses these challenges with self‑healing optimization, real‑time data pipelines, extreme concurrency handling, and seamless lakehouse access.
This article explains the core I/O components of StarRocks—Tablet, Fragment, Pipeline, Morsel, ScanOperator, ChunkSource, ScanTask, and ChunkBuffer—showing how they work together to achieve high‑performance, low‑latency query execution in a compute‑storage separated architecture.
This case study details how Vivo’s marketing automation platform evolved its data‑driven architecture—from a Presto‑based wide‑table design, through a Bitmap optimization, to a StarRocks migration—addressing performance bottlenecks, reducing resource costs, and enhancing data security.
This article dissects the full advertising funnel, explains why CTR and eCPM are critical, and demonstrates how StarRocks combined with Flink can deliver minute‑level real‑time monitoring, material selection, anomaly alerts, A/B testing, and a successful migration from Druid for massive ad‑tech workloads.
This roundup compiles the latest releases across major data platforms—including ClickHouse, Apache Fluss, Delta Lake, StarRocks, Apache Pulsar and DolphinScheduler—highlighting version numbers, key feature additions, security fixes, and emerging trends shaping the big‑data ecosystem.
Fanatics unified its fragmented data stack by building a StarRocks‑powered Lakehouse on Apache Iceberg, replacing Redshift, Snowflake, Athena, and Druid, which cut costs by up to 95%, delivered sub‑second dashboard queries on petabyte‑scale data, and enabled real‑time and historical analytics on a single platform.
This article details the deep integration of StarRocks with Apache Paimon, describing the unified architecture, version evolution, performance enhancements, time‑travel queries, native readers/writers, distributed planning, and future roadmap for achieving lakehouse‑native analytics at scale.
During Double‑11 mega‑sales, Taobao Group faced exploding OLAP query traffic, costly data sync pipelines, and slow near‑real‑time analytics, so they unified real‑time and batch data in Paimon, leveraged StarRocks for high‑performance lake queries, tuned cluster settings, and saved nearly ten‑million yuan annually while cutting refresh latency by 80%.
This article details the evolution of a data warehouse at RenliJia from a MaxCompute‑centric setup to a modern lakehouse using StarRocks, Paimon, Flink, and Fluss, describing design goals, technical evaluations, implementation steps for offline, OLAP, and real‑time workloads, and the challenges and future plans that emerged.
A big data expert from a large tech company builds a full Hadoop/Spark/CDH stack for a traditional enterprise with only hundreds of thousands of daily records, causing high maintenance costs, half-hour query delays, and eventual departure; the case underscores the importance of matching technology to business scale, with StarRocks proposed as a lightweight alternative.
This article explains how Taotian Group unified real‑time and offline data using Paimon as lake storage and StarRocks for high‑performance OLAP, eliminating costly sync pipelines, cutting refresh time by about 80%, saving nearly ten million yuan annually, and detailing the architecture, cluster safeguards, configuration tweaks, monitoring, and future roadmap for large‑scale promotional events.
This guide explains why StarRocks temporary partitions block DDL operations due to metadata consistency, locking, and atomicity constraints, and provides step-by-step commands for viewing partitions via SHOW TEMPORARY PARTITIONS and information_schema, plus safe deletion using ALTER TABLE DROP TEMPORARY PARTITION with critical precautions.
This article details Gaode Maps' end‑to‑end lakehouse solution for massive, high‑frequency trajectory data, covering the challenges of real‑time visibility, query performance, and storage cost, and explaining how a hot‑warm‑cold tiering architecture built on Apache Flink, Paimon, StarRocks, Redis and Lindorm delivers millisecond‑level queries while cutting storage expenses.
This article provides a practical framework for SMEs to select and implement data platform architectures, comparing traditional warehouses, lakehouse, and multimodal data lakes across cost, ROI, and operational efficiency, with scenario-based recommendations and a lightweight lakehouse implementation guide using open-source stack Flink, Paimon, StarRocks, and MinIO.
Veteran data‑warehouse expert explains how the Paimon‑StarRocks stack solves the write‑read split, cuts storage costs, and delivers real‑time analytics, comparing it with Hudi, Iceberg, ClickHouse and Trino, and shows why leading Chinese tech firms are adopting this lakehouse architecture.
This article details Gaode Maps' end‑to‑end lakehouse solution for handling high‑frequency, high‑volume trajectory data, covering the challenges of real‑time visibility, multi‑scenario queries, storage cost, and data silos, and describing the layered storage architecture, performance validation, and future expansion plans.
This article details a StarRocks cluster crash caused by users continuously refreshing reconciliation dashboards, the emergency response including query timeout reduction and compute node scaling, root cause analysis highlighting storage-compute separation benefits, and long-term preventive measures like business reporting processes, Multi-Warehouse isolation, and query governance.
StarRocks and Apache Paimon have been progressively integrated across multiple releases, enabling a unified lakehouse architecture that supports multi-source federated analysis, time-travel queries, native readers/writers, distributed planning, and advanced profiling, while delivering performance gains that bring Paimon query speed on par with native StarRocks tables.
This article explains how dbt drives automated data modeling and governance, how DataOps practices bring agility and control to data projects, and how StarRocks’ lakehouse architecture enables real‑time and batch analytics, illustrated with concrete workflows, version‑control conventions, and enterprise case studies.
A StarRocks case study shows how early null filtering and Bloom indexes reduced a 200M-row query from 12s to under 1s while eliminating CN node OOM restarts through predicate pushdown and partition pruning.
This article analyzes why StarRocks multi-table joins on Paimon/Iceberg tables remain significantly slower than internal tables even after cache warming, detailing six root causes across storage format, data layout, caching, execution engine, metadata, and version management, plus four optimization strategies to narrow the gap.
Facing Postgres overload and costly Snowflake queries, Fresha rebuilt its analytics platform by introducing StarRocks as a unified SQL entry point, combining federated lakehouse queries with high‑performance internal tables, which reduced homepage query latency to around 200 ms and achieved minute‑level data freshness across real‑time, historical, and search workloads.
This article explains how StarRocks mitigates the hidden risks of massive one‑time data imports in a storage‑compute separated architecture by redesigning the write path to spill to local disk, merge centrally, and write to object storage, resulting in fewer small files, higher write throughput, and more stable query performance.
Ctrip migrated its massive User Behavior Tracking system from ClickHouse to a compute‑storage separated StarRocks cluster on Kubernetes, achieving millisecond‑level query latency, halving storage usage, reducing node count, and sustaining millions‑of‑rows‑per‑second write throughput while simplifying scaling and operations.
A reproducible evaluation of StarRocks using the open‑source Coffee‑shop Benchmark shows that across 500 M, 1 B and 5 B row scales, StarRocks completes 17 complex join and aggregation queries 2–10× faster and with significantly lower cost than ClickHouse, Snowflake and Databricks, demonstrating superior performance and cost efficiency for analytical workloads.
This article chronicles a ten‑year evolution of a self‑built big data platform—detailing early Hadoop clusters, successive migrations to Spark, Hive, Hudi, and StarRocks, the operational challenges encountered, and the comprehensive shift to Alibaba Cloud EMR Serverless that delivered significant cost, performance, and stability gains while outlining future intelligent‑ecosystem plans.
The article benchmarks StarRocks internal tables against Paimon and Iceberg lake tables using TPCH 100G, shows internal tables excel at real-time analytics while lake tables optimize cold storage cost and multi-engine sharing, and details optimization strategies (metadata, data cache, predicate pushdown) achieving 5-10x speedup, illustrated by a retail case study cutting query latency from seconds to milliseconds and storage cost by 65%.
StarRocks 4.0 introduces FlatJSON, a columnar storage and execution engine that converts high‑frequency JSON fields into native columns, dramatically reducing I/O and CPU costs and enabling JSON queries to run with performance close to that of traditional columnar data.
This article details Cisco Webex's migration from a complex Pinot‑Trino OLAP stack to StarRocks, covering the challenges of the legacy system, the step‑by‑step migration process—including storage, compute, and SQL dialect transformation—and the resulting performance gains, cost reductions, and operational improvements.
StarRocks 3.5 introduces a cluster‑level snapshot mechanism that automates backup to object storage, supports minute‑level recovery, and integrates with Kubernetes via Helm charts to streamline disaster‑recovery workflows for high‑availability workloads.
This article details a production Flink CDC server_id conflict caused by copied configurations, explaining how to identify duplicate server_id/server_uuid via MySQL SHOW PROCESSLIST, assign unique identifiers, clear Flink checkpoint state, and verify resolution through logs and replication monitoring.
Since its 2016 launch, Alibaba Cloud EMR has transformed from a basic open‑source Hadoop service into a high‑performance, AI‑enabled big‑data platform, delivering optimized I/O, vectorized processing, and integrated AI functions such as natural‑language SQL, StarRocks and Spark enhancements, while supporting diverse industry workloads.
Ctrip's User Behavior Tracking (UBT) system, handling 30 TB of daily data, moved from ClickHouse to StarRocks' compute‑storage separated architecture, cutting average query latency from 1.4 seconds to 203 ms, halving storage, reducing nodes from 50 to 40, and boosting write throughput to 3 million rows per second.
Zepto transformed its brand‑analytics platform from a Postgres MVP into a production‑grade, sub‑second real‑time analytics solution by adopting StarRocks, redesigning its data pipeline with Databricks, Kafka, and Flink, and choosing a storage‑compute architecture that supports massive joins and rapid insights.
This guide details the four index types in StarRocks 3.3—Bitmap, Bloom Filter, N-Gram Bloom Filter, and Full-Text Inverted Index—covering their core principles, supported data types, creation syntax, ideal use cases (low/high cardinality, fuzzy/long-text queries), anti-patterns, and real-world optimization examples with performance metrics.
This case study details how Shuhe Technology, a leading fintech company, overcame data redundancy, low resource utilization, and slow reporting by adopting Alibaba Cloud EMR Serverless StarRocks for a unified, real‑time data warehouse, achieving standardized data pipelines, cost savings, and minute‑level decision latency.
This article explains how the Tianji advertising platform consolidated heterogeneous MySQL, ClickHouse, and TiDB data sources into a single StarRocks data warehouse, addressing data silos, real‑time performance, and query complexity while improving accuracy, latency, and development efficiency.
Facing massive data volumes, complex component dependencies, high TCO, and real‑time processing limits, the政采云 platform replaced its Hadoop stack with StarRocks’ minimalist, decoupled architecture, achieving lower costs, elastic scaling, faster queries, easier operations, and robust fault tolerance across diverse government procurement workloads.
Facing minute‑level decision demands and billions of marketing events during Taobao's Flash Sale, the Ele.me data team built a real‑time lakehouse with StarRocks and Paimon, leveraging asynchronous materialized views, RoaringBitmap de‑duplication, and resource isolation to achieve sub‑second query latency, lower storage costs, and stable high‑concurrency.
This guide explains how to recover accidentally dropped databases, tables, and partitions in StarRocks using the RECOVER command, covering the 1-day default retention window, configuration options, limitations like TRUNCATE and FORCE drops, and practical step-by-step examples with renaming strategies.
Faced with billions of marketing events and minute‑level decision requirements during Taobao's flash‑sale campaign, the e‑commerce data team built a real‑time lakehouse using StarRocks and Paimon, leveraged asynchronous materialized views and RoaringBitmap deduplication, and achieved sub‑second query latency, massive cost savings, and stable high‑concurrency performance.
In StarRocks compute-storage separation clusters, Stream Load achieves 20-30x faster bulk data ingestion than INSERT by leveraging batch-oriented design, direct compute-node access, single-transaction metadata handling, large-file storage writes, and concentrated resource scheduling, avoiding INSERT's per-row overheads.
This article compares StarRocks' integrated (BE) and separated (CN + object storage) architectures, detailing write flows like Stream Load with MemTable and compaction, read flows with local vs cached data, and scalability trade-offs.
This article explains the roles of StarRocks FE and BE nodes, details the metadata stored in FE, describes the leader‑follower‑observer architecture, and shows how BDB JE replication, journal logs, and checkpoint mechanisms ensure metadata synchronization and durability even after node failures.
This case study details how Hypergryph leveraged Alibaba Cloud EMR Serverless StarRocks, Flink, and Kafka to replace a ClickHouse data warehouse with a high‑performance, elastic, and easy‑to‑operate real‑time analytics platform that dramatically improved query speed, stability, operational efficiency, and cost for their gaming business.
Facing daily data growth from millions to 150 billion records, Joydata‑U transformed its analytics platform through three architectural stages—Hadoop, Hadoop + Trino, and finally StarRocks—introducing resource isolation, Flat JSON acceleration, and Bitmap indexing to cut query latency by up to seven times and achieve sub‑2‑minute data freshness across BI, ad‑tech, game analytics, and CRM workloads.
The article explains the purpose, configuration, and core implementation of StarRocks' TabletChecker component, detailing how it periodically scans OlapTable tablets, evaluates their health through multiple checks, and hands unhealthy tablets to the TabletScheduler for repair.
This article details Qunar's transition to StarRocks as a unified OLAP engine, covering the business background, engine evaluation, architecture redesign, observability, high‑availability strategies, query‑performance optimizations, real‑world application cases, community contributions, and future plans.
This article explores the evolution of business intelligence toward intelligent BI, detailing traditional BI limitations, agile BI improvements, and how StarRocks' MPP lakehouse engine combined with large language models enables natural‑language analytics, real‑time performance, AI‑driven insights, and scalable enterprise deployments.
This article details how Quark’s data platform consolidated multiple analytics engines into a unified StarRocks‑based OLAP solution, covering business background, engine selection, architecture redesign, performance tuning, operational practices, and future plans for scalability and reliability.
This step‑by‑step tutorial shows how to deploy StarRocks and MinIO in a decoupled storage‑compute architecture using Docker Compose and Kubernetes, configure local caching, create storage volumes, load public datasets, and run SQL queries to explore the combined data.
Shopee combined the Paimon data lake with StarRocks and Flink to create a quasi‑real‑time warehouse, enabling fast task diagnostics and a high‑performance financial reconciliation system while dramatically reducing storage costs and latency through innovative ODS, snapshot, and branch table techniques.
This guide demonstrates using StarRocks Routine Load to ingest JSON and CSV data from Kafka, covering environment setup, table creation, load job configuration with critical parameters like jsonpaths, troubleshooting null-field errors, task management commands, and performance optimization tips for real-time analytics.
Suixingfu rebuilt its payment data pipeline by replacing a fragmented Lambda stack with a unified Porter CDC + StarRocks + Elasticsearch architecture, achieving three‑fold query speed, ten‑fold analytics efficiency, 20% storage reduction, and sub‑second data‑capture latency across high‑concurrency, ad‑hoc, and batch workloads.
StarRocks 3.5 introduces a cluster‑level Snapshot backup for fast recovery, a bulk‑load optimization that reduces small files and compaction cost, smarter partition management with time‑based merging and TTL, multi‑statement transactions with full ACID guarantees, low‑cardinality dictionary support for lake tables, and several security and performance upgrades.
This article details Shopee's Data Infra team's use of the Paimon data lake to build near‑real‑time warehouses, accelerate ODS layers, implement a task‑diagnosis system, and create a reconciliation platform, while sharing future plans and a Q&A session.
This guide outlines the StarRocks SRCA certification exam, highlights essential study resources, breaks down the core topics such as architecture, data import/export, SQL optimization and performance tuning, and offers practical tips, mock‑exam details, and personal experience to help candidates succeed.
This article examines how Xiaohongshu replaced its costly Lambda architecture with a real‑time lakehouse built on Iceberg, Paimon, Spark, and StarRocks, achieving minute‑level latency, higher data quality, lower resource consumption, and dramatically faster query performance.
This article details how Alibaba's A+ traffic analysis platform tackled trillion‑row log ingestion and high‑concurrency queries by redesigning storage with Paimon, leveraging Flink for real‑time ingestion, and using StarRocks for fast lake analytics, ultimately reducing query latency from minutes to seconds.
StarRocks MCP Server provides a universal adapter that lets large language models like Claude, OpenAI, and Gemini execute SQL queries directly against StarRocks, simplifying data Q&A, intelligent analysis, and automated reporting by eliminating the need for bespoke plugins or complex prompt engineering.
Grab replaced its fragmented Grafana‑Superset stack with a StarRocks‑backed Iris platform, achieving over ten‑fold query speedups, 40% lower resource usage, and a unified real‑time and historical data store for Spark observability across its Southeast Asian super‑app ecosystem.
This article details troubleshooting StarRocks write failures caused by THRIFT_EAGAIN timeouts, analyzing JVM memory patterns, Thrift RPC behavior, TRUNCATE TABLE resource overhead, and resolving the issue by increasing thrift_rpc_timeout_ms from 5s to 30s while planning long-term architectural improvements.
This article details a comprehensive DataOps implementation using Alibaba Cloud's DataWorks, Flink, StarRocks, MaxCompute, and Yunxiao pipeline, covering four-layer architecture, standardized development, automated CI/CD with gray releases, full-chain quality monitoring achieving 80% self-healing, cross-team collaboration, cost optimization, and a phased rollout roadmap delivering 30% faster delivery and 30% cost reduction.
Facing a flood of data from over 8,000 communities, the Bifeng service team migrated from a monolithic storage‑compute architecture to a StarRocks‑based storage‑compute separation solution, achieving lower costs, higher resource utilization, faster queries, and improved SLA across their property management platform.
This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its evolution, core architecture, multi‑stage optimization pipeline, key optimizations such as multi‑join colocate, low‑cardinality global dictionary, MV union rewrite, and advanced mechanisms like cost‑estimation fixes, query feedback, adaptive execution, runtime filters, join‑reorder strategies, and SQL plan management.
This guide explains how to design a comprehensive monitoring and alerting framework for StarRocks, covering resource usage, service availability, and business continuity with practical PromQL queries and troubleshooting steps.
This article explains the fundamentals of Retrieval‑Augmented Generation, demonstrates how to create and query vector indexes using StarRocks, shows how DeepSeek provides embeddings and answer generation, and walks through a complete end‑to‑end RAG pipeline with code examples and a web UI.
This guide details deploying a StarRocks compute-storage separation cluster using Alibaba Cloud OSS, covering architecture design, resource planning, Linux optimization, FE and CN node deployment with Ansible automation, configuration parameters, post-deployment tuning, and verification steps.
The author decommissioned three StarRocks nodes with over 1M tablets each; default replica migration moved only 200k replicas in 24 hours, so they increased tablet_sched_slot_num_per_path from 2 to 32 per disk during off-peak, achieving 800k+ replicas migrated per node in 8 hours while monitoring disk I/O.
JD Logistics transformed its one‑stop self‑service analytics platform, UData, by migrating from an integrated storage‑compute architecture to a storage‑compute separated design powered by StarRocks, achieving sub‑10‑second P95/P99 query latency, reducing storage costs by 90%, and cutting compute expenses around 30% while supporting massive data volumes.
The author documents three StarRocks production issues: varchar(50) truncating MaxCompute strings, BE memory spikes caused by API gateway timeout mismatches, and a global dictionary error fixed by disabling low-cardinality optimization.
The article details how StarRocks extends the Apache ORC C++ library to decrypt column‑level encrypted ORC files, describing the file hierarchy, AES‑128‑CTR key handling, the query‑time master‑key retrieval, a decorator‑based decryption/decompression pipeline, and the block‑skip‑read mechanism that enables efficient predicate push‑down.
This step‑by‑step guide explains how to use EMR Serverless Spark together with the StarRocks Spark Connector to create a workspace, upload the connector JAR, configure network connections, create databases and tables in StarRocks, and perform read/write operations via SQL sessions, Notebook sessions, or batch Spark jobs, complete with code examples and UI screenshots.
NAVER migrated its massive analytics platform from ClickHouse to StarRocks, achieving dramatic improvements in multi‑table JOIN performance, real‑time aggregation speed, and horizontal scalability while simplifying data integration across heterogeneous sources on a Kubernetes‑based architecture.
This article details iQIYI's transition from Impala+Kudu and ClickHouse to StarRocks, describing the OLAP architecture, performance gains of up to 400% in advertising workloads, the technical challenges of data consistency, lake‑warehouse fusion, operational scaling, and the step‑by‑step migration process using a dual‑run platform.
