Apache Calcite vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Calcite

Stacks11

Followers29

Votes0

GitHub Stars5.0K

Forks2.4K

Apache Calcite vs Apache Spark: What are the differences?

Introduction:

Apache Calcite and Apache Spark are both open-source projects that are widely used in the field of big data processing and analytics. While they have some similarities, there are key differences between them that make each suitable for different use cases. In this markdown, we will outline six specific differences between Apache Calcite and Apache Spark.

Architecture: Apache Calcite is primarily a SQL parser and optimizer framework. It provides a flexible and extensible architecture for building SQL engines and query optimization tools. On the other hand, Apache Spark is a full-fledged big data processing engine that combines distributed computing, SQL-like queries, and machine learning capabilities in a unified platform.
Processing Paradigm: Apache Calcite is a pull-based processing engine, which means that it processes the data by pulling it from the data sources based on the SQL queries. It applies optimizations and transformations on the data as it is being pulled. In contrast, Apache Spark is a push-based processing engine, where the data processing tasks are pushed to the data nodes for parallel execution.
Data Model: Apache Calcite provides a relational model for data processing, where the data is organized in tables with rows and columns. It supports SQL queries and operations such as joins, aggregations, and filtering on these tables. In contrast, Apache Spark supports a more flexible data model, including support for structured, semi-structured, and unstructured data. It provides APIs for working with structured data using DataFrames and Datasets, and also provides RDD abstraction for low-level access and processing of data.
In-memory Processing: Apache Calcite relies on the underlying execution engine to handle the actual execution of the SQL queries. It can work on top of various execution engines such as Apache Flink, Apache Beam, or even Apache Spark. Apache Spark, on the other hand, has its own in-memory processing engine that provides efficient execution of data processing tasks.
Streaming Support: Apache Spark has native support for processing structured streaming data in real-time. It provides a high-level API for consuming and processing streaming data, and it can seamlessly integrate with other Spark components such as Spark SQL, MLlib, and GraphX for advanced analytics and machine learning tasks. While Apache Calcite can handle streaming data, it does not provide native support for real-time streaming processing.
Advanced Analytics: Apache Spark is designed to handle a wide range of big data processing tasks, including advanced analytics and machine learning. It provides libraries and APIs for performing data analysis, machine learning, graph processing, and stream processing. Apache Calcite, on the other hand, focuses primarily on query optimization and does not provide built-in support for advanced analytics tasks.

In Summary, Apache Calcite is a SQL parser and optimizer framework with a relational data model, primarily used for query optimization, whereas Apache Spark is a full-fledged big data processing engine with support for distributed computing, SQL-like queries, machine learning, and stream processing.

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Advice on Apache Spark, Apache Calcite

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Apache Calcite
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	It is an open source framework for building databases and data management systems. It includes a SQL parser, an API for building expressions in relational algebra, and a query planning engine
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Sql parsing; Query optimization
Statistics
GitHub Stars 42.2K	GitHub Stars 5.0K
GitHub Forks 28.9K	GitHub Forks 2.4K
Stacks 3.1K	Stacks 11
Followers 3.5K	Followers 29
Votes 140	Votes 0
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	No community feedback yet
Integrations
No integrations available	jQuery MySQL MongoDB SQLite

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Related Comparisons

Apache Calcite vs Apache Spark: What are the differences?

Introduction:

Architecture: Apache Calcite is primarily a SQL parser and optimizer framework. It provides a flexible and extensible architecture for building SQL engines and query optimization tools. On the other hand, Apache Spark is a full-fledged big data processing engine that combines distributed computing, SQL-like queries, and machine learning capabilities in a unified platform.
Processing Paradigm: Apache Calcite is a pull-based processing engine, which means that it processes the data by pulling it from the data sources based on the SQL queries. It applies optimizations and transformations on the data as it is being pulled. In contrast, Apache Spark is a push-based processing engine, where the data processing tasks are pushed to the data nodes for parallel execution.
Data Model: Apache Calcite provides a relational model for data processing, where the data is organized in tables with rows and columns. It supports SQL queries and operations such as joins, aggregations, and filtering on these tables. In contrast, Apache Spark supports a more flexible data model, including support for structured, semi-structured, and unstructured data. It provides APIs for working with structured data using DataFrames and Datasets, and also provides RDD abstraction for low-level access and processing of data.
In-memory Processing: Apache Calcite relies on the underlying execution engine to handle the actual execution of the SQL queries. It can work on top of various execution engines such as Apache Flink, Apache Beam, or even Apache Spark. Apache Spark, on the other hand, has its own in-memory processing engine that provides efficient execution of data processing tasks.
Streaming Support: Apache Spark has native support for processing structured streaming data in real-time. It provides a high-level API for consuming and processing streaming data, and it can seamlessly integrate with other Spark components such as Spark SQL, MLlib, and GraphX for advanced analytics and machine learning tasks. While Apache Calcite can handle streaming data, it does not provide native support for real-time streaming processing.
Advanced Analytics: Apache Spark is designed to handle a wide range of big data processing tasks, including advanced analytics and machine learning. It provides libraries and APIs for performing data analysis, machine learning, graph processing, and stream processing. Apache Calcite, on the other hand, focuses primarily on query optimization and does not provide built-in support for advanced analytics tasks.

Apache Calcite vs Apache Spark

Overview