Apache Spark vs Druid

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Druid

Stacks376

Followers867

Votes32

Apache Spark vs Druid: What are the differences?

Introduction:

Apache Spark and Druid are two popular open-source tools used for big data processing and analytics. While both tools are designed to handle large volumes of data, they have key differences in terms of architecture, data processing capabilities, and use cases.

1. Data Processing Model:

Apache Spark is a distributed computing system that offers in-memory processing and supports batch processing, real-time streaming, and machine learning workloads. It provides a high-level API for developers to write distributed data processing applications.

Druid, on the other hand, is a real-time analytics database specifically designed for handling time-series data. It is optimized for fast querying and aggregation of large datasets and provides low-latency access to real-time data.

2. Workload Types:

Spark is well-suited for a wide range of data processing use cases, including batch processing, real-time streaming, machine learning, and graph processing. It can handle both structured and unstructured data and can scale horizontally by adding more compute resources.

Druid, on the other hand, is specifically designed for use cases that require fast aggregation and querying of time-series data, such as monitoring real-time metrics, event analytics, and log analysis. It is not designed for transactional data processing or complex analytics.

3. Data Storage and Indexing:

Spark uses a distributed file system, like Hadoop HDFS, for storing data and supports various file formats like Parquet, Avro, and ORC. It relies on a distributed computing model where data is loaded into memory for processing.

Druid, on the other hand, has its own column-oriented storage format that is optimized for time-series data. It uses a combination of memory and disk-based storage to achieve fast querying and provides different indexing strategies like bitmap indexes and inverted indexes.

4. Querying Capabilities:

Spark provides a SQL-like interface called Spark SQL that allows users to run SQL queries on structured data. It also offers APIs in different programming languages like Scala, Java, and Python for more flexibility. Spark SQL can handle both batch and real-time queries.

Druid, on the other hand, uses a custom querying language known as Druid Query Language (DSL), which is designed specifically for querying time-series data. It supports fast aggregations, filtering, and complex queries on large datasets.

5. Scalability:

Spark is known for its scalability and ability to handle large-scale data processing. It can scale horizontally by adding more nodes to the cluster, allowing it to process petabytes of data. Spark also provides built-in fault-tolerance mechanisms for handling failures.

Druid is designed to scale horizontally as well, but it is more optimized for low-latency, real-time queries on time-series data. It achieves high query throughput by leveraging distributed data storage, parallel processing, and caching techniques.

6. Ecosystem Integration:

Spark has a rich ecosystem with support for various data sources, connectors, and libraries. It integrates well with other components in the Hadoop ecosystem, including HDFS, Hive, and HBase. It also provides connectors for popular databases like MySQL, PostgreSQL, and Cassandra.

Druid also has a growing ecosystem, but its focus is primarily on real-time analytics use cases. It provides connectors for data sources like Kafka and supports integration with visualization tools like Superset and Tableau.

In summary, Apache Spark and Druid are both powerful tools for big data processing and analytics. Spark is a general-purpose distributed computing system suitable for a wide range of workloads, while Druid is specifically optimized for real-time analytics on time-series data.

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Advice on Apache Spark, Druid

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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Comments

Detailed Comparison

Apache Spark	Druid
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.	Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
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	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 376
Followers 3.5K	Followers 867
Votes 140	Votes 32
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 15 Real Time Aggregations 6 Batch and Real-Time Ingestion 5 OLAP 3 OLAP + OLTP 2 Combining stream and historical analytics Cons 3 Limited sql support 2 Joins are not supported well 1 Complexity
Integrations
No integrations available	Zookeeper

What are some alternatives to Apache Spark, Druid?

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Apache Kudu

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

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