Alternatives to Beringei logo

Alternatives to Beringei

InfluxDB, Redis, Hazelcast, Aerospike, and SAP HANA are the most popular alternatives and competitors to Beringei.
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What is Beringei and what are its top alternatives?

Beringei is a high performance time series storage engine. Time series are commonly used as a representation of statistics, gauges, and counters for monitoring performance and health of a system.
Beringei is a tool in the In-Memory Databases category of a tech stack.
Beringei is an open source tool with 3.1K GitHub stars and 303 GitHub forks. Here’s a link to Beringei's open source repository on GitHub

Top Alternatives to Beringei

  • InfluxDB

    InfluxDB

    InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • 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. ...

  • Aerospike

    Aerospike

    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. ...

  • 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. ...

  • 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 ...

  • 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. ...

  • Azure Redis Cache

    Azure Redis Cache

    It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern. ...

Beringei alternatives & related posts

InfluxDB logo

InfluxDB

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904
163
An open-source distributed time series database with no external dependencies
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PROS OF INFLUXDB
  • 51
    Time-series data analysis
  • 28
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 18
    Real-time analytics
  • 6
    Continuous Query support
  • 5
    Easy Query Language
  • 4
    HTTP API
  • 4
    Out-of-the-box, automatic Retention Policy
  • 1
    Offers Enterprise version
  • 1
    Free Open Source version
CONS OF INFLUXDB
  • 4
    Instability
  • 1
    HA or Clustering is only in paid version

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Hi everyone. I'm trying to create my personal syslog monitoring.

  1. To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

  2. To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

I would like to know... Which is a cheaper and scalable solution?

Or even if there is a better way to do it.

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Redis logo

Redis

43.3K
32.4K
3.9K
An in-memory database that persists on disk
43.3K
32.4K
+ 1
3.9K
PROS OF REDIS
  • 875
    Performance
  • 535
    Super fast
  • 511
    Ease of use
  • 441
    In-memory cache
  • 321
    Advanced key-value cache
  • 190
    Open source
  • 179
    Easy to deploy
  • 163
    Stable
  • 153
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 32
    Very Scalable
  • 23
    Replication
  • 20
    Great community
  • 19
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Async replication
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Bitmaps
  • 6
    Open Source
  • 6
    Keys with a limited time-to-live
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Awesomeness for Free!
  • 4
    Hyperloglogs
  • 3
    outstanding performance
  • 3
    Runs server side LUA
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 3
    Transactions
  • 2
    Data structure server
  • 2
    Performance & ease of use
  • 1
    Existing Laravel Integration
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Object [key/value] size each 500 MB
  • 1
    Simple
  • 1
    Channels concept
  • 1
    Scalable
  • 1
    Temporarily kept on disk
  • 1
    Dont save data if no subscribers are found
  • 0
    Jk
CONS OF REDIS
  • 14
    Cannot query objects directly
  • 2
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

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Hazelcast logo

Hazelcast

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56
Clustering and highly scalable data distribution platform for Java
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+ 1
56
PROS OF HAZELCAST
  • 10
    High Availibility
  • 6
    Distributed Locking
  • 5
    Distributed compute
  • 5
    Sharding
  • 4
    Load balancing
  • 3
    Sql query support in cluster wide
  • 3
    Map-reduce functionality
  • 3
    Written in java. runs on jvm
  • 3
    Publish-subscribe
  • 2
    Performance
  • 2
    Simple-to-use
  • 2
    Multiple client language support
  • 2
    Rest interface
  • 2
    Optimis locking for map
  • 1
    Super Fast
  • 1
    Admin Interface (Management Center)
  • 1
    Better Documentation
  • 1
    Easy to use
CONS OF HAZELCAST
  • 3
    License needed for SSL

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Aerospike logo

Aerospike

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Flash-optimized in-memory open source NoSQL database
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+ 1
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PROS OF AEROSPIKE
  • 11
    Ram and/or ssd persistence
  • 10
    Easy clustering support
  • 5
    Easy setup
  • 3
    Acid
  • 2
    Performance better than Redis
  • 2
    Petabyte Scale
  • 2
    Scale
  • 1
    Ease of use
CONS OF AEROSPIKE
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    SAP HANA logo

    SAP HANA

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    An in-memory, column-oriented, relational database management system
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    PROS OF SAP HANA
    • 5
      In-memory
    • 5
      SQL
    • 4
      Distributed
    • 4
      Performance
    • 2
      Realtime
    • 2
      Concurrent
    • 2
      OLAP
    • 2
      OLTP
    • 1
      JSON
    CONS OF SAP HANA
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      Apache Ignite logo

      Apache Ignite

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      118
      20
      An open-source distributed database, caching and processing platform
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      20
      PROS OF APACHE IGNITE
      • 3
        Written in java. runs on jvm
      • 3
        Free
      • 2
        High Avaliability
      • 2
        Rest interface
      • 2
        Sql query support in cluster wide
      • 2
        Multiple client language support
      • 2
        Load balancing
      • 1
        Easy to use
      • 1
        Better Documentation
      • 1
        Distributed compute
      • 1
        Distributed Locking
      CONS OF APACHE IGNITE
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        related Apache Ignite posts

        MemSQL logo

        MemSQL

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        Database for real-time transactions and analytics.
        72
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        + 1
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        PROS OF MEMSQL
        • 6
          Distributed
        • 3
          Realtime
        • 2
          Sql
        • 2
          JSON
        • 2
          Concurrent
        • 2
          Columnstore
        • 1
          Scalable
        • 1
          Ultra fast
        CONS OF MEMSQL
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          Azure Redis Cache logo

          Azure Redis Cache

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          A fully managed, open source–compatible in-memory data store
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          PROS OF AZURE REDIS CACHE
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            CONS OF AZURE REDIS CACHE
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