A very common reason is a wrong site baseUrl configuration.\n

Current configured baseUrl = / (default value)\n

We suggest trying baseUrl = \n\n';var e=document.getElementById("__docusaurus-base-url-issue-banner-suggestion-container"),s=window.location.pathname,r="/"===s.substr(-1)?s:s+"/";e.innerHTML=r}}window.__DOCUSAURUS_INSERT_BASEURL_BANNER=!0,document.addEventListener("DOMContentLoaded",maybeInsertBanner)

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Scaling Bitcoin data to billions of records with Apache Doris: our journey to auto-partitioning
Best Practice

Scaling Bitcoin data to billions of records with Apache Doris: our journey to auto-partitioning

To power lightning-fast queries and ensure the Deep Dive dashboards deliver real-time insights, Ortege relies on Apache Doris. A crucial feature they embrace is Auto Partition.

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Fine-tuning Apache Doris for maximum performance and resilience: a deep dive into fe.conf
Best Practice

Fine-tuning Apache Doris for maximum performance and resilience: a deep dive into fe.conf

Ortege handles massive volumes of blockchain data to power its analytics platform, Ortege Studio. Apache Doris forms the backbone of its Lakehouse v2, enabling it to process billions of records and deliver real-time insights.

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New milestone: Apache Doris 3.0 has been released
Release Note

New milestone: Apache Doris 3.0 has been released

Starting from version 3.X, Apache Doris supports a compute-storage decoupled mode inaddition to the compute-storage mode for cluster deployment.

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Real-time data warehouse in TikTok based on Apache Doris
Best Practice

Real-time data warehouse in TikTok based on Apache Doris

We are excited to invite the data platform team of TikTok to talk about how they use Apache Doris in their real-time data architecture and how they benefit from it, which could serve as a model for effective real-time data warehousing.

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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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