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Apache Doris 2.1.10 Released
Release Note

Apache Doris 2.1.10 Released

This version introduces continuous upgrades and enhancements in Query Execution Engine and Lakehouse. It also resolves critical bugs to improve stability and performance.

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How Tencent Music saved 80% in costs by migrating from Elasticsearch to Apache Doris
Best Practice

How Tencent Music saved 80% in costs by migrating from Elasticsearch to Apache Doris

Handle full-text search, audience segmentation, and aggregation analysis directly within Apache Doris and slash their storage costs by 80% while boosting write performance by 4x

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Slash your cost by 90% with Apache Doris Compute-Storage Decoupled Mode
Tech Sharing

Slash your cost by 90% with Apache Doris Compute-Storage Decoupled Mode

Apache Doris compute-storage decoupled mode achieves 90% cost reduction and provides elasticity and workload isolation, while maintaining high performance in data ingestion and queries.

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Why Apache Doris is a Better Alternative to Elasticsearch for Real-Time Analytics
Tech Sharing

Why Apache Doris is a Better Alternative to Elasticsearch for Real-Time Analytics

Apache Doris is a real-time data warehouse commonly used for observability, cyber security analysis, online reports, customer profiles, data lakehouse and more. Elasticsearch is more like a search engine, but it is also widely used for data analytics, so there's an overlap in their use cases. The comparison in this post will focus on the real-time analytics capabilities of Apache Doris and Elasticsearch from a user-oriented perspective

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What is Apache Doris

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Core capabilities

Real-time ingestion and storage
Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation.
Lightning-fast query
Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine.
Federated querying
Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL.
Semi-structured data
Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches.
Elastic architecture
Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.
Open ecosystem
Compatible with MySQL protocol and ANSI SQL, easily integrated with BI tools. Provide open data API to be accessible for external compute engines like Spark, Flink and ML/AI.

Unified data warehouse

for various analytics use cases

From traditional batch reporting to real-time reporting and dashboards. From internal-facing analytics like traditional BI to customer-facing analytics. From decision support analytics to algorithm-driven real-time decision-making.
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