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Spark Connect
Spark Connect is a protocol that specifies how a client application can communicate with a remote Spark Server. Clients that implement the Spark Connect protocol can connect and make requests to remote Spark Servers, very similar to how client applications can connect to databases using a JDBC driver — a query spark.table("some_table").limit(5) should simply return the result. This architecture gives end users a great developer experience
Supported Languages for Spark Connect:
- Python
- Java
- Scala 3
- Go
- Rust
Materialized Views
A powerful feature aimed at significantly improving query performance and data management for large-scale data analytics.
- Improved Query Performance: Materialized views store the pre-computed results of complex queries. This means that instead of re-executing the entire query logic every time data is requested, the system can retrieve the pre-computed results directly, leading to much faster data retrieval. This is particularly beneficial for Spark Structured Streaming, where real-time data processing with low latency is crucial (Apache Issues) (Cloudera Blog).
- Efficient Data Management: By allowing pre-aggregation and transformation of data, materialized views simplify subsequent queries, making them more efficient. They also reduce data movement across the network by storing views closer to where the data is consumed, further enhancing performance (Apache Issues) (Cloudera Blog).
- Simplified Workflows: Developers can leverage predefined materialized views that encapsulate specific business logic. This not only reduces development time but also promotes code reuse. Materialized views act as reusable components that simplify complex data processing tasks (Apache Issues) (The Apache Software Foundation Blog).
- Consistency and Maintenance: Ensuring that materialized views reflect the latest data state is crucial. Spark 4.0 addresses this with options for both full and incremental rebuilds of views. Incremental rebuilds are particularly efficient, processing only the changes since the last update, thereby saving on processing time and resources (Cloudera Blog).
- Integration with Other Features: Materialized views in Spark 4.0 are designed to integrate seamlessly with other advanced features such as Apache Iceberg tables. This integration supports advanced snapshot management, partition-level operations, and other optimizations that enhance the overall data processing capabilities of Spark (Apache Wiki).
ANSI SQL and Collation Support
Apache Spark 4.0 introduces enhanced support for ANSI SQL, including features such as collation, which significantly improve the platform’s SQL capabilities and align it more closely with SQL standards used in traditional databases.
- ANSI SQL Compliance: ANSI SQL mode by default. This ensures that SQL queries executed in Spark are compliant with ANSI SQL standards, which improves data quality and integrity. It also helps avoid data inconsistencies that might arise from non-compliant operations. This mode enforces stricter rules for SQL operations, providing more predictable and standardized behavior for SQL queries (Apache Spark) (Apache Issues).
- Collation Support: Collation defines how string comparison is performed, which can affect sorting and filtering operations. This is particularly important for ensuring that queries involving string data behave consistently across different environments and match the expected locale-specific rules for text processing (Databricks) (Apache Spark).
- Expanded SQL Capabilities: Enhances its SQL capabilities with new features such as SQL Cache V2, UDF (User Defined Functions) improvements, and better integration for complex data workflows. These improvements make it easier to perform advanced data processing and analytics directly within Spark SQL(Databricks).
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Python Data Source API
Simple API in Python for Data Sources. The idea is to enable Python developers to create data sources without having to learn Scala or deal with the complexities of the current data source APIs. The goal is to make a Python-based API that is simple and easy to use, thus making Spark more accessible to the wider Python developer community. This proposed approach is based on the recently introduced Python user-defined table functions (SPARK-43797) with extensions to support data sources.
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