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When to Scale PostgreSQL Analytics? Advancing Analytics without unnecessary tool sprawl
Tech Sharing

When to Scale PostgreSQL Analytics? Advancing Analytics without unnecessary tool sprawl

PostgreSQL handles OLTP well, but analytics workloads often push teams to bolt on multiple specialized systems. This post covers the signs it is time to scale out your analytics and how Apache Doris can consolidate those workloads into one platform.

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From ClickHouse + Elasticsearch to Apache Doris: How Kwai Unified Trillion-Scale Ad Analytics
Best Practice

From ClickHouse + Elasticsearch to Apache Doris: How Kwai Unified Trillion-Scale Ad Analytics

Kwai, a short-video platform with over 400 million daily active users, migrated its advertising analytics from ClickHouse and Elasticsearch to Apache Doris, achieving up to 90% latency reduction and 3x write throughput.

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Why a Mexican Mining Giant Migrated from Azure Synapse to Apache Doris
Best Practice

Why a Mexican Mining Giant Migrated from Azure Synapse to Apache Doris

One of largest mining companies in Mexicomigrated from Azure Synapse to Apache Doris to solve unpredictable cloud costs and slow query performance across its 20,000-employee operation.

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How Xanh SM, Leading EV ride hailer in Vietnam, Built Real-Time Recommendations with Apache Doris
Best Practice

How Xanh SM, Leading EV ride hailer in Vietnam, Built Real-Time Recommendations with Apache Doris

Xanh SM, Leading EV ride hailer in Vietnam, built a real-time personalized location recommendation system with Apache Doris to power smarter driver-rider matching.

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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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