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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.
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.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Calcite
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Apache Calcite
Cons of Apache Spark
- Speed4
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What is Apache Calcite?
What is Apache Spark?
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