Apache Doris, renowned for its prowess in real-time analytics, owes much of its efficiency and scalability to its robust storage engine. In this deep technical blog, we’ll dissect the inner workings of Doris’s storage engine, exploring its architecture, data storage model, optimization techniques, and fault tolerance strategies.
1. Columnar Storage Architecture:
Storage Layout:
- Apache Doris employs a columnar storage layout, where data is organized and stored column-wise rather than row-wise.
- This architecture offers several advantages, including efficient compression, improved query performance, and reduced I/O overhead.
Compression Techniques:
- Doris utilizes various compression techniques such as dictionary encoding, run-length encoding, and delta encoding to minimize storage footprint and optimize query performance.
- These compression techniques are applied at both the column level and the block level to achieve maximum efficiency.
Encoding Schemes:
- Different encoding schemes are used for different data types to optimize storage and query performance.
- For example, integer and timestamp columns may use dictionary encoding, while floating-point columns may use delta encoding.
2. Partitioning and Distribution:
Partitioning Strategy:
- Data in Apache Doris is partitioned and distributed across nodes in the cluster based on partition keys.
- Partitioning enables parallel processing and efficient resource utilization, as each node is responsible for processing a subset of the data.
Distribution Keys:
- Doris supports distribution keys, which determine how data is distributed across nodes in the cluster.
- Distribution keys are chosen based on factors such as data skew, query patterns, and performance considerations to achieve balanced data distribution and optimal query performance.
3. Data Replication:
Replication Strategies:
- Apache Doris supports various data replication strategies, including synchronous and asynchronous replication.
- Replication ensures fault tolerance and data durability by maintaining multiple copies of data across nodes in the cluster.
Automatic Failover:
- In the event of node failures or network partitions, Doris automatically triggers failover mechanisms to ensure continuous availability and data consistency.
- Data is redistributed to healthy nodes, and query execution is rerouted to avoid disruptions in service.
4. Data Lifecycle Management:
Compaction and Cleanup:
- Doris includes mechanisms for data compaction and cleanup to optimize storage utilization and improve query performance.
- Old and obsolete data can be compacted or purged to reclaim storage space and maintain optimal performance.
Storage Tiering:
- Apache Doris supports storage tiering, where data is stored in different tiers based on access frequency and retention policies.
- Hot data may be stored in high-performance storage tiers, while cold data may be moved to lower-cost storage tiers for cost optimization.
5. Fault Tolerance and High Availability:
Redundancy and Resilience:
- Doris ensures fault tolerance and high availability by replicating data across multiple nodes and data centers.
- Redundancy and resilience mechanisms minimize the impact of node failures and network partitions, ensuring continuous operation and data integrity.
Continuous Monitoring:
- Continuous monitoring of cluster health, node status, and data consistency is essential for detecting and mitigating faults in real-time.
- Doris employs monitoring tools and alerting mechanisms to proactively identify and address issues before they impact service availability.
Conclusion:
The storage engine in Apache Doris serves as the backbone of its real-time analytics capabilities, providing efficient data storage, retrieval, and management. By mastering the intricacies of Doris’s storage engine architecture, organizations can unlock the full potential of their data, achieving unparalleled performance, scalability, and reliability in their analytics workflows.