Apache Doris, an open-source distributed SQL data warehousing system, stands at the forefront of real-time analytics solutions, offering scalability, performance, and fault tolerance. In this technical blog, we’ll embark on a journey through the inner workings of Apache Doris, exploring its distributed architecture, data storage model, query execution mechanisms, and fault tolerance strategies.
1. Distributed Architecture:
Frontend Component:
The frontend component of Apache Doris is responsible for parsing SQL queries, performing query optimization, and generating an execution plan. It interacts with client applications, abstracting the complexities of query processing.
Backend Component:
- Metadata Management: The backend manages metadata such as table schemas, partitions, and distribution keys. It ensures consistency across the cluster and facilitates efficient query planning.
- Query Coordination: This component coordinates query execution across distributed nodes, dividing query fragments and managing intermediate results exchange.
- Cluster Monitoring: Continuous monitoring of cluster health, resource utilization, and node status is vital for optimizing performance and ensuring fault tolerance.
Storage Engine:
- Columnar Storage: Data in Apache Doris is stored in a columnar format, offering efficient compression and fast query performance.
- Partitioning: Data is partitioned and distributed across nodes based on partition keys. This enables parallel processing and efficient resource utilization.
- Data Replication: Replicating data across nodes ensures fault tolerance and data durability. Doris supports various replication strategies to meet different use case requirements.
Query Engine:
- Parallel Execution: Queries are executed in parallel across distributed nodes, leveraging the collective computing power of the cluster.
- Vectorized Processing: Apache Doris utilizes vectorized processing techniques to perform operations on entire columns of data simultaneously, maximizing CPU efficiency and query throughput.
- Optimized Operators: Doris includes optimized query operators for common operations such as joins, aggregations, and filters, minimizing memory usage and CPU overhead.
2. Data Ingestion:
Batch Ingestion:
- Apache Doris supports batch ingestion from various sources such as data lakes, databases, or files.
- Efficient mechanisms for bulk loading and partitioning data ensure optimal performance and resource utilization.
- Data is loaded into the cluster, distributed across nodes based on partition keys, and processed in parallel for scalable ingestion.
Streaming Ingestion:
- Doris seamlessly integrates with streaming data sources like Apache Kafka or Apache Pulsar for real-time data ingestion.
- Continuous ingestion of streaming data enables organizations to analyze and derive insights from live data streams in real-time.
- Doris provides connectors and integration options for reliable and efficient streaming ingestion.
3. Query Execution and Optimization:
- Doris optimizes query execution through techniques such as query rewriting, join reordering, and predicate pushdown to minimize query execution time and resource usage.
- A cost-based optimizer estimates query costs and selects the optimal execution plan based on factors like data distribution, query complexity, and available resources.
- Intermediate query results are cached and reused to minimize redundant computations, improving query performance.
4. Fault Tolerance and High Availability:
- Doris employs data replication, automatic failover, and recovery mechanisms to ensure fault tolerance and data durability.
- Continuous monitoring of cluster health and node status enables automatic redistribution of data and resources to maintain availability and performance.
- Failures are detected and mitigated in real-time, minimizing downtime and ensuring continuous operation.
Conclusion:
Apache Doris exemplifies the pinnacle of real-time analytics solutions with its distributed architecture, efficient query execution mechanisms, and fault tolerance strategies. By understanding its technical intricacies, organizations can harness the power of Apache Doris to build scalable, reliable, and efficient data analytics solutions for deriving actionable insights from their data in real-time.