⚡Apache Spark Structured Streaming

NEW

A scalable, fault-tolerant stream processing engine integrated with Spark SQL supporting micro-batch and continuous processing.

DESCRIPTION

Structured Streaming treats streaming data as incremental append-only tables using the Spark SQL engine. It provides high-level declarative APIs with event-time semantics, watermarks, and windowed aggregations. Fault-tolerance relies on checkpointing and state recovery. Integrations include Kafka, files, Delta Lake, and cloud storage. It supports micro-batch and continuous processing modes with exactly-once guarantees under supported sinks. Monitoring and recovery are managed via the streaming query API. It simplifies building robust pipelines for real-time analytics and ETL.

ALTERNATIVE DEFINITIONS

Apache Spark

Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine.

Highlights scalability, fault tolerance, and Spark SQL integration.

Source

Databricks

Structured Streaming is a stream processing engine built on the Spark SQL engine.

Emphasizes Spark SQL foundation and stream processing capabilities.

Source

RELATED TERMS

Apache Spark Streaming Query Event Time Watermark Windowing Checkpointing Exactly-Once Semantics Micro-batch Apache Kafka Delta Lake

COMPETING TERMS

Apache Flink Apache Beam Kafka Streams

RELATIONS

• Broader: Spark Structured Streaming
• Derived from: Apache Spark

KNOWLEDGE AREAS

Real-time & Streaming Data Data Engineering Data Services Infrastructure

TARGET AUDIENCE

Data Engineer MLOps Engineer Software Developer Cloud Architect

EXTERNAL SOURCES

• Wikipedia - Apache Spark
• Apache Spark - Structured Streaming overview
• Apache Spark - PySpark streaming API
• Delta Lake - Reliable streaming storage

TOP QUESTIONS

How does Apache Spark Structured Streaming model streams?

As append-only tables, enabling SQL operations and incremental computation over unbounded data.

Category: Concept

What is event time in Apache Spark Structured Streaming?

Timestamp from the event itself, used with watermarks to handle late data.

Category: Concept

How does Apache Spark Structured Streaming achieve fault tolerance?

Through checkpointing, write-ahead logs, and state recovery across failures.

Category: Resilience

What sinks support exactly-once in Apache Spark Structured Streaming?

Sinks like Delta Lake support idempotent writes and transactional guarantees.

Category: Management

What are common mistakes with Apache Spark Structured Streaming?

Ignoring backpressure, misconfigured watermarks, and non-idempotent sinks causing duplicates.

Category: Mistakes

How do micro-batches differ from continuous processing?

Micro-batches process small batches; continuous processing aims for lower latency with streaming operators.

Category: Difference

How to monitor Apache Spark Structured Streaming jobs?

Use StreamingQuery events, metrics, logs, and external observability tools.

Category: HowTo

What integrations are typical for Apache Spark Structured Streaming?

Kafka, cloud storage, Delta Lake, and JDBC sinks for real-time pipelines.

Category: Integration

What hot topics surround Apache Spark Structured Streaming?

Lakehouse architectures and scalable stateful aggregations with watermarks.

Category: Topic