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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Aerospike vs Apache Spark

Aerospike vs Apache Spark

OverviewDecisionsComparisonAlternatives

Overview

Aerospike
Aerospike
Stacks200
Followers288
Votes48
GitHub Stars1.3K
Forks196
Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K

Aerospike vs Apache Spark: What are the differences?

# Introduction
This markdown compares Aerospike and Apache Spark based on key differences.

1. **Storage System**: Aerospike is a NoSQL database primarily designed for real-time high-performance use cases, while Apache Spark is a distributed computing system that includes a storage layer for processing large datasets.
   
2. **Data Processing**: Aerospike focuses on fast and efficient data storage and retrieval with support for ACID transactions, whereas Apache Spark is built for processing and analyzing big data sets in a distributed manner using in-memory computations and lazy evaluation.
   
3. **Programming Language**: Aerospike supports multiple client libraries in various languages like Java, Python, and C#, whereas Apache Spark primarily operates using the Scala programming language, with support for Java, Python, and R as well.

4. **Data Processing Models**: Aerospike follows a key-value data model that is schema-less, while Apache Spark operates on Resilient Distributed Datasets (RDDs) and DataFrames, enabling structured data processing and manipulation through a higher-level API.

5. **Processing Speed**: Aerospike provides extremely low latency for reads and writes due to its optimized architecture, making it suitable for real-time applications, whereas Apache Spark is better suited for batch processing and interactive queries but may not perform as well in real-time scenarios.

6. **Use Cases**: Aerospike is well-suited for ad tech, recommendation engines, and other real-time applications where low latency is critical, while Apache Spark is commonly used in data analytics, machine learning, and ETL (extract, transform, load) workflows where distributed processing is required.

In Summary, Aerospike is focused on fast real-time storage and retrieval with ACID transactions, while Apache Spark excels in distributed data processing and analytics using in-memory computations.

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Advice on Aerospike, Apache Spark

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.

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Detailed Comparison

Aerospike
Aerospike
Apache Spark
Apache Spark

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.

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.

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.
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
1.3K
GitHub Stars
42.2K
GitHub Forks
196
GitHub Forks
28.9K
Stacks
200
Stacks
3.1K
Followers
288
Followers
3.5K
Votes
48
Votes
140
Pros & Cons
Pros
  • 16
    Ram and/or ssd persistence
  • 12
    Easy clustering support
  • 5
    Easy setup
  • 4
    Acid
  • 3
    Scale
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    Great for distributed SQL like applications
  • 8
    One platform for every big data problem
  • 6
    Easy to install and to use
Cons
  • 4
    Speed

What are some alternatives to Aerospike, Apache Spark?

Redis

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Hazelcast

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

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

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

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

Apache Flink

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

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

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

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.

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