Apache Drill vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Drill

Stacks74

Followers171

Votes16

Apache Drill vs Apache Spark: What are the differences?

Introduction:

Apache Drill and Apache Spark are both powerful open-source big data processing frameworks. While they share some similarities, they also have significant differences in terms of capabilities and use cases. In this comparison, we will highlight the key differences between Apache Drill and Apache Spark.

Data Processing Paradigm: Apache Drill is designed to provide real-time interactive query capabilities on various data sources, including structured and semi-structured data. It supports schema-free data exploration and querying on top of different storage systems without the need for pre-defined schemas or ETL processes. On the other hand, Apache Spark is a unified analytics engine that provides distributed data processing capabilities, supporting batch processing, interactive queries, and streaming. It follows a batch-based data processing paradigm and requires data to be structured or semi-structured with a pre-defined schema.
Query Optimizations: Apache Drill leverages the advantages of schema-free data by performing on-the-fly schema discovery and pushing down query operations to the storage layer. It uses a query execution engine that is aware of the data's structure and optimizes the queries accordingly, providing interactive response times. In contrast, Apache Spark optimizes query execution by applying various query optimization techniques, such as predicate pushdown, join reordering, and column pruning. It optimizes the execution plan based on the data's schema and parallelizes the processing across a cluster, ensuring efficient data processing for large-scale data.
Supported Data Sources: Apache Drill supports a wide range of data sources, including traditional databases (e.g., MySQL, PostgreSQL), NoSQL databases (e.g., MongoDB, HBase), file systems (e.g., HDFS, S3), and cloud storage (e.g., Google Cloud Storage, Azure Blob Storage). It can query and join data across different data sources seamlessly. On the other hand, Apache Spark supports similar data sources but also provides connectors for additional sources such as message queues (e.g., Apache Kafka) and real-time streaming sources (e.g., Apache Kafka, Apache Flume).
Ease of Use: Apache Drill is designed to have a schema-free approach and provides an SQL-like query language for querying various data sources. It simplifies the data exploration process, as it does not require upfront schema definition or complex ETL workflows. However, it may have limitations in handling complex data transformations or optimizing advanced analytic queries. In contrast, Apache Spark provides a rich set of APIs in multiple programming languages (Scala, Java, Python, R) that allow developers to perform complex data processing tasks and build sophisticated analytics applications. It provides more flexibility and control over the data processing logic and has extensive support for advanced analytics and machine learning.
Performance and Scalability: Apache Drill emphasizes low latency and real-time query capabilities, making it suitable for interactive data exploration and querying on large datasets. It achieves performance gains by leveraging parallel processing and distributed query execution across a cluster. Apache Spark, on the other hand, provides high throughput and scalability for batch processing and large-scale data analysis. It achieves scalability by distributing the data and computation across the cluster, allowing it to handle massive datasets.
Ecosystem and Integration: Apache Drill integrates well with the Apache Hadoop ecosystem, providing seamless integration with tools like Apache Hive, Apache HBase, and Apache Parquet. It can leverage the data storage and processing capabilities of these tools to provide a unified querying experience. In contrast, Apache Spark has a comprehensive ecosystem and supports integration with various tools and frameworks, including Hadoop, Hive, HBase, Spark SQL, and machine learning libraries like MLlib and TensorFlow. It provides a unified and streamlined experience for big data processing, analytics, and machine learning.

In Summary, Apache Drill and Apache Spark differ in their data processing paradigms, query optimization techniques, supported data sources, ease of use, performance and scalability, and ecosystem integrations. Apache Drill offers a schema-free, real-time interactive querying experience on diverse data sources, while Apache Spark provides a unified analytics engine with a broader range of data processing capabilities, advanced analytics, and machine learning support.

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Advice on Apache Spark, Apache Drill

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 Drill
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.	Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
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	Low-latency SQL queries;Dynamic queries on self-describing data in files (such as JSON, Parquet, text) and MapR-DB/HBase tables, without requiring metadata definitions in the Hive metastore.;ANSI SQL;Nested data support;Integration with Apache Hive (queries on Hive tables and views, support for all Hive file formats and Hive UDFs);BI/SQL tool integration using standard JDBC/ODBC drivers
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 74
Followers 3.5K	Followers 171
Votes 140	Votes 16
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	Pros 4 NoSQL and Hadoop 3 Lightning speed and simplicity in face of data jungle 3 Free 2 Well documented for fast install 1 Read Structured and unstructured data

What are some alternatives to Apache Spark, Apache Drill?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Presto

Distributed SQL Query Engine for Big Data

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Related Comparisons

Apache Drill vs Apache Spark: What are the differences?

Introduction:

Data Processing Paradigm: Apache Drill is designed to provide real-time interactive query capabilities on various data sources, including structured and semi-structured data. It supports schema-free data exploration and querying on top of different storage systems without the need for pre-defined schemas or ETL processes. On the other hand, Apache Spark is a unified analytics engine that provides distributed data processing capabilities, supporting batch processing, interactive queries, and streaming. It follows a batch-based data processing paradigm and requires data to be structured or semi-structured with a pre-defined schema.
Query Optimizations: Apache Drill leverages the advantages of schema-free data by performing on-the-fly schema discovery and pushing down query operations to the storage layer. It uses a query execution engine that is aware of the data's structure and optimizes the queries accordingly, providing interactive response times. In contrast, Apache Spark optimizes query execution by applying various query optimization techniques, such as predicate pushdown, join reordering, and column pruning. It optimizes the execution plan based on the data's schema and parallelizes the processing across a cluster, ensuring efficient data processing for large-scale data.
Supported Data Sources: Apache Drill supports a wide range of data sources, including traditional databases (e.g., MySQL, PostgreSQL), NoSQL databases (e.g., MongoDB, HBase), file systems (e.g., HDFS, S3), and cloud storage (e.g., Google Cloud Storage, Azure Blob Storage). It can query and join data across different data sources seamlessly. On the other hand, Apache Spark supports similar data sources but also provides connectors for additional sources such as message queues (e.g., Apache Kafka) and real-time streaming sources (e.g., Apache Kafka, Apache Flume).
Ease of Use: Apache Drill is designed to have a schema-free approach and provides an SQL-like query language for querying various data sources. It simplifies the data exploration process, as it does not require upfront schema definition or complex ETL workflows. However, it may have limitations in handling complex data transformations or optimizing advanced analytic queries. In contrast, Apache Spark provides a rich set of APIs in multiple programming languages (Scala, Java, Python, R) that allow developers to perform complex data processing tasks and build sophisticated analytics applications. It provides more flexibility and control over the data processing logic and has extensive support for advanced analytics and machine learning.
Performance and Scalability: Apache Drill emphasizes low latency and real-time query capabilities, making it suitable for interactive data exploration and querying on large datasets. It achieves performance gains by leveraging parallel processing and distributed query execution across a cluster. Apache Spark, on the other hand, provides high throughput and scalability for batch processing and large-scale data analysis. It achieves scalability by distributing the data and computation across the cluster, allowing it to handle massive datasets.
Ecosystem and Integration: Apache Drill integrates well with the Apache Hadoop ecosystem, providing seamless integration with tools like Apache Hive, Apache HBase, and Apache Parquet. It can leverage the data storage and processing capabilities of these tools to provide a unified querying experience. In contrast, Apache Spark has a comprehensive ecosystem and supports integration with various tools and frameworks, including Hadoop, Hive, HBase, Spark SQL, and machine learning libraries like MLlib and TensorFlow. It provides a unified and streamlined experience for big data processing, analytics, and machine learning.

Apache Drill vs Apache Spark

Overview