Aerospike vs Apache Flink

Overview

Aerospike

Stacks200

Followers288

Votes48

GitHub Stars1.3K

Forks196

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Aerospike vs Apache Flink: What are the differences?

Introduction

Aerospike and Apache Flink are both powerful technologies used in different aspects of data processing. However, they have key differences that set them apart from each other. In this analysis, we will explore and highlight the six main distinctions between Aerospike and Apache Flink.

Data Storage: Aerospike is primarily a high-performance, distributed NoSQL database designed to handle massive amounts of data with low latency. It provides a key-value store that can be used for real-time operations. On the other hand, Apache Flink does not focus on storage itself but rather on processing data streams and batch processing. It can integrate with different storage systems, including Aerospike, to perform computations on the data.
Data Processing Model: Aerospike follows the traditional ACID (Atomicity, Consistency, Isolation, Durability) model, making it suitable for transactional workloads. It ensures that data consistency is maintained reliably. Meanwhile, Apache Flink operates on a stream processing model and provides event time processing and guaranteed exactly-once processing semantics. It is built to handle real-time streaming data efficiently.
Processing Capabilities: Aerospike focuses on high-speed read and write operations with low latency, making it suitable for real-time applications and high-traffic scenarios. It supports both OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) workloads. On the other hand, Apache Flink excels in processing large-scale data streams and batch data with fault tolerance. It provides advanced features like windowing, fault tolerance, and complex event processing.
Scale and Flexibility: Aerospike is highly scalable and can handle large datasets with millions of operations per second. It provides automatic data distribution and replication across nodes. Apache Flink also supports scalability with its distributed processing model and can be deployed on clusters of machines, allowing horizontal scalability. However, Flink's flexibility surpasses Aerospike as it can integrate with various data sources and connectors, making it adaptable to different data ecosystems.
Processing Paradigm: Aerospike follows a traditional database approach, where developers query and retrieve data using SQL-like statements or traditional data access methods. It offers secondary indexes, query aggregation, and different query types. In contrast, Apache Flink utilizes a stream-first architecture that treats batch processing as a special case. It provides window operations, stream transformations, and advanced processing functions that enhance its stream processing capabilities.
Community Support and Maturity: Aerospike has been in the market for over a decade and is widely adopted by various enterprises. It has a mature and active community, continuously improving and adding new features. On the other hand, Apache Flink has gained significant popularity in recent years and has a growing community. It benefits from the Apache Software Foundation's support, ensuring continuous development and community-driven innovation.

In summary, Aerospike and Apache Flink differ in their primary focus on data storage, processing models, capabilities, scalability, processing paradigms, and community maturity. While Aerospike specializes in real-time data storage, Flink excels in distributed stream processing and batch processing, offering greater flexibility and advanced features.

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Advice on Aerospike, Apache Flink

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

Aerospike	Apache Flink
Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.	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.
99% of reads/writes complete in under 1 millisecond.;Predictable low latency at high throughput – second to none. Read the YCSB Benchmark.;The secret sauce? A thousand things done right. Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies. Highly parallelized multi-threaded, multi-core, multi-cpu, multi-SSD execution.;Indexes are always stored in RAM. Pure RAM mode is backed by spinning disks. In hybrid mode, individual tables are stored in either RAM or flash.	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 1.3K	GitHub Stars 25.4K
GitHub Forks 196	GitHub Forks 13.7K
Stacks 200	Stacks 534
Followers 288	Followers 879
Votes 48	Votes 38
Pros & Cons
Pros 16 Ram and/or ssd persistence 12 Easy clustering support 5 Easy setup 4 Acid 3 Petabyte Scale	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
Integrations
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Aerospike, Apache Flink?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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

Distributed SQL Query Engine for Big Data

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

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.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

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.

Druid

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.

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

Related Comparisons

Aerospike vs Apache Flink: What are the differences?

Introduction

Data Storage: Aerospike is primarily a high-performance, distributed NoSQL database designed to handle massive amounts of data with low latency. It provides a key-value store that can be used for real-time operations. On the other hand, Apache Flink does not focus on storage itself but rather on processing data streams and batch processing. It can integrate with different storage systems, including Aerospike, to perform computations on the data.
Data Processing Model: Aerospike follows the traditional ACID (Atomicity, Consistency, Isolation, Durability) model, making it suitable for transactional workloads. It ensures that data consistency is maintained reliably. Meanwhile, Apache Flink operates on a stream processing model and provides event time processing and guaranteed exactly-once processing semantics. It is built to handle real-time streaming data efficiently.
Processing Capabilities: Aerospike focuses on high-speed read and write operations with low latency, making it suitable for real-time applications and high-traffic scenarios. It supports both OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) workloads. On the other hand, Apache Flink excels in processing large-scale data streams and batch data with fault tolerance. It provides advanced features like windowing, fault tolerance, and complex event processing.
Scale and Flexibility: Aerospike is highly scalable and can handle large datasets with millions of operations per second. It provides automatic data distribution and replication across nodes. Apache Flink also supports scalability with its distributed processing model and can be deployed on clusters of machines, allowing horizontal scalability. However, Flink's flexibility surpasses Aerospike as it can integrate with various data sources and connectors, making it adaptable to different data ecosystems.
Processing Paradigm: Aerospike follows a traditional database approach, where developers query and retrieve data using SQL-like statements or traditional data access methods. It offers secondary indexes, query aggregation, and different query types. In contrast, Apache Flink utilizes a stream-first architecture that treats batch processing as a special case. It provides window operations, stream transformations, and advanced processing functions that enhance its stream processing capabilities.
Community Support and Maturity: Aerospike has been in the market for over a decade and is widely adopted by various enterprises. It has a mature and active community, continuously improving and adding new features. On the other hand, Apache Flink has gained significant popularity in recent years and has a growing community. It benefits from the Apache Software Foundation's support, ensuring continuous development and community-driven innovation.

Aerospike vs Apache Flink

Overview

Aerospike vs Apache Flink: What are the differences?