Data processing and analytics have become increasingly important for businesses looking to gain insights from their data. Two popular big data analytics platforms are Amazon Redshift and Apache Spark. Redshift is a data warehouse product that is designed for online analytical processing (OLAP), while Spark is a general-purpose distributed data processing engine. There has been much debate around which platform performs faster for analytics workloads. This article will take a deep dive into the architectures of Redshift and Spark to understand why Redshift often outperforms Spark in terms of raw speed.
Columnar Data Storage
One of the key architectural differences between Redshift and Spark is how they store data on disk. Redshift employs a columnar data storage format, while Spark uses row-oriented storage. In a row-oriented store, data is organized into rows and columns within those rows. In a columnar store, data is organized into columns instead. Columnar storage provides advantages for analytics queries which commonly target specific columns rather than full rows of data:
- Lower I/O – Reading from disk is often the bottleneck during query execution. Columnar storage means that queries only need to read the specific columns accessed rather than entire rows.
- Data compression – Columnar data exhibits high information redundancy and can be compressed at much higher ratios than row data.
- Advanced indexing – Sorting data by column allows for more advanced data skipping indexes such as bitmap indexes.
By utilizing columnar storage, Redshift is able to execute queries orders of magnitude faster than row-oriented systems like Spark for analytics workloads. Redshift stores all data on disk in a compressed columnar format and employs advanced indexing and query optimization to accelerate analysts queries.
Massively Parallel Processing (MPP)
Redshift is designed from the ground up to leverage massively parallel processing (MPP) architecture to accelerate queries across large datasets. MPP systems distribute data and query execution across potentially thousands of nodes.
Some key ways Redshift leverages MPP:
- Distributed storage – Data is divided into blocks and distributed across compute nodes. This allows for parallel I/O across disks.
- Parallel queries – Queries are split into steps which are executed simultaneously on the various nodes.
- Columnar compression – Columnar data organization lends itself well to MPP. Operations on column values can be distributed across nodes.
Redshift intelligently handles all the parallelization and distribution of queries across nodes. Queries like aggregations can be massively accelerated by leveraging the aggregated power of multiple nodes. Spark can also leverage cluster scaling via parallel operations, but Redshift is purpose built for MPP SQL analytics.
Advanced Query Optimization
In addition to its MPP architecture, Redshift employs sophisticated query optimization and execution engine Trino, to intelligently plan queries. Some key optimizations include:
- Predicate filtering – Selective filter operations are accelerated by only reading relevant data blocks.
- Aggregate pruning – Performing aggregations in phases across nodes to minimize data transfer.
- Data distribution – Optimally distributing data across nodes to minimize data movement.
- Join optimization – Choosing optimized join strategies (nested loop, hash) based on data size and distribution.
Redshift generates highly tuned query plans based on query semantics and the current state of data. This allows even complex analytical queries across petabytes of data to be performed within seconds. Spark SQL employs optimizations like predicate push down, but Redshift was designed from the ground up for high performance MPP SQL analytics.
Workload Management
Running analytics concurrently across multiple users and workloads can quickly bottleneck systems without proper workload management. Redshift provides sophisticated workload management (WLM) to optimize concurrent workloads. WLM features include:
- Query queues – Queries are routed to different queues based on user groups/classes.
- Query scheduling – Queries are scheduled across queues/nodes to meet performance targets.
- Short query acceleration – Small queries are accelerated using WLM fast lanes.
This enables Redshift to maintain high performance even under heavy concurrent usage across users and varying query loads. Spark does not provide native workload management capabilities out of the box. Cluster sharing across users or groups can quickly degrade performance if not managed properly.
Caching and Materialization
Repeated queries against static datasets can cause unnecessary processing. Redshift provides advanced caching and materialization capabilities to avoid this:
- Result caching – Reuses cached results for repeated queries.
- Materialized views – Results of expensive joins/aggregations can be stored as tables.
Spark caches some intermediate data in memory by default, but has less native support for advanced caching constructs like materialized views. The ability to materialize and reuse intermediate results provides significant acceleration for common access patterns in production workloads.
Serverless Elasticity
Redshift provides automatic scaling capabilities to adapt to changing workload needs:
- Elastic resize – Cluster can scale out without interrupting running queries.
- Auto scaling – Redshift can automatically add nodes during spikes in demand.
- Concurrency scaling – New cluster resources spun up for individual queries.
This provides Redshift significant flexibility to elastically scale to accommodate workload changes. Spark workloads have to be managed more manually to scale cluster resources appropriately.
Data Ingest and Integration
Getting data efficiently loaded into the warehouse is essential for analytics. Redshift provides robust tooling for ETL and integration:
- Batch loading – Load data in parallel from S3, DynamoDB, and DMS.
- Streaming – Continuously stream new data from Kinesis, Kafka.
- Python and R integration – Native SDKs for directly interfacing with data.
These capabilities allow analysts to easily ingest petabytes of structured or semi-structured data into Redshift from a variety of sources. Doing the same at scale with Spark requires building more custom ingestion pipelines.
Machine Learning Integration
Once data is loaded into Redshift, users can leverage it for machine learning workflows:
- SageMaker integration – Invoke SageMaker models directly with SQL.
- ML functions – Apply ML models natively within Redshift SQL.
This enables a powerful ML lifecycle with Redshift as the central data hub between model training and deployment. While Spark MLlib provides data science functionality, Redshift makes it easy to operationalize models back on analytical data.
Business Intelligence Integration
Redshift integrates tightly with a wide array of BI tools that analysts know and love:
- Tableau
- Looker
- Quicksight
These tools provide intuitive analytics UIs/visuzalizations without requiring data movement. Spark integration with BI tools is often more limited or may require custom coding.
Summary
In summary, Redshift is designed from the ground up to provide fast, scalable SQL analytics on big data. Its architecture makes smart tradeoffs that optimize for large read-only analytical workloads. Key advantages include:
- Columnar storage for accelerated I/O.
- Massively parallel query execution.
- Advanced MPP query optimization.
- Workload management for consistent performance.
- Caching/materialization to avoid reprocessing.
- Elastic scaling to accommodate spikes.
- First class integrations for data load, ML, and BI.
Spark provides distributed data processing capabilities but is designed more generally as a compute engine. While Spark SQL has made the engine more accessible for analytics, Redshift still outperforms it handily for highly concurrent analytical workloads. Companies with demanding analytics needs are better served by Redshift’s purpose built architecture.