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  1. Stackups
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  5. Apache Flink vs Druid

Apache Flink vs Druid

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Druid
Druid
Stacks376
Followers867
Votes32

Apache Flink vs Druid: What are the differences?

Introduction

This Markdown code provides a comparison between Apache Flink and Druid, highlighting the key differences between these two technologies.

  1. Processing Model: Apache Flink is a stream processing technology that provides both batch and stream processing capabilities. It supports event-time and processing-time semantics, and allows for stateful operations, windowing, and event-time processing. On the other hand, Druid is a real-time analytical database designed to handle high-throughput, low-latency querying of time-series data. It relies on a columnar data store and is optimized for OLAP style queries.

  2. Data Ingestion: Apache Flink supports various data sources and connectors, allowing data to be ingested from multiple systems. It provides built-in connectors for Kafka, Hadoop file systems, and more. Flink also supports custom sources and sinks, making it flexible for different use cases. Druid, on the other hand, relies on a specific data ingestion architecture where data is ingested through real-time and batch ingestion processes. It provides connectors for popular data sources like Kafka, Hadoop, and more.

  3. Query Capabilities: Apache Flink provides a rich set of operators and functions for processing and analyzing data. It supports SQL queries, batch and stream processing, and complex event processing. Flink also allows for iterative processing and machine learning with its built-in libraries. Druid, on the other hand, focuses on OLAP-style queries for time-series data. It is optimized for fast aggregations and filtering on large datasets, making it suitable for real-time analytics.

  4. Scalability and Fault-tolerance: Apache Flink is designed to scale horizontally and can handle large amounts of data. It provides fault-tolerance through its distributed and reliable streaming architecture, allowing for high data availability and resilience in the face of failures. Druid is also designed to scale horizontally and can handle large datasets. It provides fault-tolerance through data replication and distributed query processing, ensuring high availability and reliability.

  5. Data Storage and Indexing: Apache Flink does not have its own storage layer and can work with various storage systems like Hadoop Distributed File System (HDFS) or Amazon S3. It does not provide indexing capabilities out of the box. Druid, on the other hand, has its own columnar storage format and indexing capabilities built-in. It uses inverted indexes and bitmap indexes to optimize queries and speed up data retrieval.

  6. Use Cases: Apache Flink is commonly used for real-time stream processing, batch processing, and building complex event processing applications. It is widely used in industries like e-commerce, finance, and telecommunications. Druid, on the other hand, is commonly used for real-time analytics and powering interactive dashboards. It is well-suited for use cases like monitoring, ad tech, and IoT analytics.

In Summary, Apache Flink is a flexible stream processing technology with batch processing capabilities, while Druid is a real-time analytical database optimized for time-series data querying. Flink provides more general-purpose processing capabilities, while Druid is specialized for OLAP-style queries on large datasets.

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Advice on Apache Flink, Druid

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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.

576k views576k
Comments

Detailed Comparison

Apache Flink
Apache Flink
Druid
Druid

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.

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.

Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
-
Statistics
GitHub Stars
25.4K
GitHub Stars
-
GitHub Forks
13.7K
GitHub Forks
-
Stacks
534
Stacks
376
Followers
879
Followers
867
Votes
38
Votes
32
Pros & Cons
Pros
  • 16
    Unified batch and stream processing
  • 8
    Easy to use streaming apis
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 4
    Open Source
  • 2
    Low latency
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
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
Zookeeper
Zookeeper

What are some alternatives to Apache Flink, Druid?

Apache Spark

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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.

lakeFS

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

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

Splunk

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

Apache Impala

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

Vertica

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

Azure Synapse

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

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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