Alternatives to Kapacitor logo

Alternatives to Kapacitor

Grafana, Kafka, Apache Spark, Prometheus, and Telegraf are the most popular alternatives and competitors to Kapacitor.
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What is Kapacitor and what are its top alternatives?

It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.
Kapacitor is a tool in the Stream Processing category of a tech stack.

Top Alternatives to Kapacitor

  • Grafana
    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • 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. ...

  • Prometheus
    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • Telegraf
    Telegraf

    It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics. ...

  • Riemann
    Riemann

    Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU. ...

  • 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. ...

  • Flux
    Flux

    Flux is the application architecture that Facebook uses for building client-side web applications. It complements React's composable view components by utilizing a unidirectional data flow. It's more of a pattern rather than a formal framework, and you can start using Flux immediately without a lot of new code. ...

Kapacitor alternatives & related posts

Grafana logo

Grafana

12.8K
10.1K
402
Open source Graphite & InfluxDB Dashboard and Graph Editor
12.8K
10.1K
+ 1
402
PROS OF GRAFANA
  • 84
    Beautiful
  • 67
    Graphs are interactive
  • 57
    Free
  • 56
    Easy
  • 33
    Nicer than the Graphite web interface
  • 24
    Many integrations
  • 17
    Can build dashboards
  • 10
    Easy to specify time window
  • 9
    Dashboards contain number tiles
  • 8
    Can collaborate on dashboards
  • 5
    Open Source
  • 5
    Click and drag to zoom in
  • 5
    Integration with InfluxDB
  • 4
    Authentification and users management
  • 4
    Threshold limits in graphs
  • 3
    It is open to cloud watch and many database
  • 3
    Simple and native support to Prometheus
  • 2
    Great community support
  • 2
    Alerts
  • 2
    You can visualize real time data to put alerts
  • 2
    You can use this for development to check memcache
  • 0
    Grapsh as code
  • 0
    Plugin visualizationa
CONS OF GRAFANA
  • 1
    No interactive query builder

related Grafana posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

https://eng.uber.com/m3/

(GitHub : https://github.com/m3db/m3)

See more
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 14 upvotes · 203K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
Kafka logo

Kafka

17.8K
16.9K
587
Distributed, fault tolerant, high throughput pub-sub messaging system
17.8K
16.9K
+ 1
587
PROS OF KAFKA
  • 125
    High-throughput
  • 118
    Distributed
  • 88
    Scalable
  • 82
    High-Performance
  • 65
    Durable
  • 37
    Publish-Subscribe
  • 19
    Simple-to-use
  • 16
    Open source
  • 11
    Written in Scala and java. Runs on JVM
  • 7
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 4
    KSQL
  • 3
    Robust
  • 2
    Suport Multiple clients
  • 2
    Partioned, replayable log
  • 1
    Flexible
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Fun
CONS OF KAFKA
  • 29
    Non-Java clients are second-class citizens
  • 27
    Needs Zookeeper
  • 7
    Operational difficulties
  • 2
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

See more
Apache Spark logo

Apache Spark

2.6K
3K
137
Fast and general engine for large-scale data processing
2.6K
3K
+ 1
137
PROS OF APACHE SPARK
  • 59
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 7
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    In memory Computation
  • 2
    Machine learning libratimery, Streaming in real
CONS OF APACHE SPARK
  • 3
    Speed

related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.1M views

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

See more
Prometheus logo

Prometheus

2.9K
3.2K
238
An open-source service monitoring system and time series database, developed by SoundCloud
2.9K
3.2K
+ 1
238
PROS OF PROMETHEUS
  • 46
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring
CONS OF PROMETHEUS
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    TLS is quite difficult to understand
  • 2
    Requires multiple applications and tools
  • 1
    Single point of failure

related Prometheus posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

https://eng.uber.com/m3/

(GitHub : https://github.com/m3db/m3)

See more
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 14 upvotes · 203K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
Telegraf logo

Telegraf

205
244
16
The plugin-driven server agent for collecting & reporting metrics
205
244
+ 1
16
PROS OF TELEGRAF
  • 5
    One agent can work as multiple exporter with min hndlng
  • 5
    Cohesioned stack for monitoring
  • 2
    Open Source
  • 2
    Metrics
  • 1
    Supports custom plugins in any language
  • 1
    Many hundreds of plugins
CONS OF TELEGRAF
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    related Telegraf posts

    Riemann logo

    Riemann

    34
    53
    9
    A network monitoring system
    34
    53
    + 1
    9
    PROS OF RIEMANN
    • 5
      Sophisticated stream processing DSL
    • 4
      Clojure-based stream processing
    CONS OF RIEMANN
      Be the first to leave a con

      related Riemann posts

      Apache Flink logo

      Apache Flink

      440
      715
      36
      Fast and reliable large-scale data processing engine
      440
      715
      + 1
      36
      PROS OF APACHE FLINK
      • 15
        Unified batch and stream processing
      • 8
        Easy to use streaming apis
      • 8
        Out-of-the box connector to kinesis,s3,hdfs
      • 3
        Open Source
      • 2
        Low latency
      CONS OF APACHE FLINK
        Be the first to leave a con

        related Apache Flink posts

        Surabhi Bhawsar
        Technical Architect at Pepcus · | 7 upvotes · 579.6K views
        Shared insights
        on
        KafkaKafkaApache FlinkApache Flink

        I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

        See more

        I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

        See more
        Flux logo

        Flux

        479
        483
        130
        Application Architecture for Building User Interfaces
        479
        483
        + 1
        130
        PROS OF FLUX
        • 44
          Unidirectional data flow
        • 32
          Architecture
        • 19
          Structure and Data Flow
        • 14
          Not MVC
        • 12
          Open source
        • 6
          Created by facebook
        • 3
          A gestalt shift
        CONS OF FLUX
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          related Flux posts

          Marcos Iglesias
          Sr. Software Engineer at Eventbrite · | 13 upvotes · 171.6K views

          We are in the middle of a change of the stack on the front end. So we used Backbone.js with Marionette. Then we also created our own implementation of a Flux kind of flow. We call it eb-flux. We have worked with Marionette for a long time. Then at some point we start evolving and end up having a kind of Redux.js-style architecture, but with Marionette.

          But then maybe one and a half years ago, we started moving into React and that's why we created the Eventbrite design system. It's a really nice project that probably could be open sourced. It's a library of components for our React components.

          With the help of that library, we are building our new stack with React and sometimes Redux when it's necessary.

          See more