platform VS ClickHouse

Compare platform vs ClickHouse and see what are their differences.

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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platform ClickHouse
3 216
25 35,595
- 2.1%
10.0 10.0
over 3 years ago 6 days ago
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of platform. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-21.
  • Ask HN: Which books/resources to understand modern Assembler?
    6 projects | | 21 Apr 2024
    The wiki[0] links some resources. The intel optimization manual[1] is also useful.

    These resources are mostly aimed at solving problems for which compilers are not very useful, so there are probably other resources that are a better fit.



  • SWAR find any byte from set
    2 projects | | 8 Mar 2023
    The web site containing the article has a collection of other articles about this sort of thing. You can also read about this in some parts of Algorithms for Modern Hardware[0]. includes some other links about this stuff[1]. In general there isn't a great guide and it's best to get your hands dirty. happens to be a good place to do that :)



  • Cache invalidation is one of the hardest problems in computer science
    2 projects | | 26 Nov 2022
    There are a lot of issues here, so I can share some stuff about some of them and hope that some helpful internet commenters come along and point out where I have neglected important things.

    A single modern CPU core is superscalar and has a deep instruction pipeline. With your help, it will decode and reorder many instructions and execute many instructions concurrently. Each of those instructions can operate on a lot of data.

    As famous online controversial opinion haver Casey Muratori tells us, most software just sucks, like really really bad (e.g. commonly people will post hash table benchmarks of high-performance hash tables that do bulk inserts in ~100ns/op, but you can do <10ns/op easily if you try), and using SIMD instructions is table stakes for making good use of the machinery inside of a single CPU core. SIMD instructions are not just for math! They are tools for general purpose programming, and when your program has unpredictable branches in it, it is often a lot cheaper to compute both branches and have a data dependency than to have a branch. Instructions like pshufb or blendv or just using a dang lookup table can replace branches. Wojciech Muła's web site[0] is the best collection of notes about using SIMD instructions for general-purpose programming, but I have found many of the articles to be incomplete or incorrect, and I have not yet done anything to fix the issue. "Using SIMD" ends up meaning that you choose the low-level layout of your data to be more suitable to processing using the instructions available, not just replacing the "glue code" and leaving the data the same as it was.

    Inside your single CPU core there is hardware for handling virtual -> physical address translation. This is a special cache called the translation lookaside buffer (TLB). Normally, chips other than recent Apple chips have a couple hundred entries of 1 4KiB page each in the TLB, and recent Apple chips have a couple hundred entries of 1 16KiB page each. Normal programs deal with a bit more than 1 meg of RAM today, and as a result they spend a huge portion of their execution time on TLB misses. You can fix this by using explicit huge pages on Linux. This feature nominally exists on Windows but is basically unusable for most programs because it requires the application to run as administrator and because the OS will never compact memory once it is fragmented (so the huge pages must be obtained at startup and never released, or they will disappear until you reboot). I have not tried it on Mac. As an example of a normal non-crazy program that is helped by larger pages, one person noted[1] that Linux builds 16% faster on 16K vs on 4K pages.

    Inside your single CPU core is a small hierarchy of set-associative caches. With your help, it will have the data it needs in cache almost all the time! An obvious aspect of this is that when you need to work on some data repeatedly, if you have a choice, you should do it before you have worked on a bunch of other data and caused that earlier data to be evicted (that is, you can rearrange your work to avoid "capacity misses"). A less obvious aspect of this is that if you operate on data that is too-aligned, you will greatly reduce the effective size of your cache, because all the data you are using will go into the same tiny subset of your cache! Famous online good opinion haver Dan Luu wrote about this here[2]. The links included in that post are also good resources for the topics you've asked about.

    When coordinating between multiple CPU cores, as noted in TFA, it is helpful to avoid false sharing[3]. People in industry have mostly found that it is helpful to avoid sharing *at all*, which is why they have work explicitly divided among cores and communicate over queues rather than dumping things into a concurrent hash map and hoping things work out. In general this is not a popular practice, and if you go online and post stuff like "Well, just don't allocate any memory after startup and don't pass any data between threads other than by using queues" you will lose imaginary internet points.

    There are some incantations you may want to apply if you would like Linux to prioritize running your program, which are documented in the Red Hat Low Latency Performance Tuning guide[4] and Erik Rigtorp's web site[5].

    Some other various resources are[6], a web site where you can practice this sort of thing, a list of links associated with[7], Sergey Slotin's excellent online book[8], and Dendi Bakh's online course[9] and blog[10].

    > Off topic: What level of sophistication about modern CPUs is _good_ to have?

    Probably none? These skills are basically unemployable as far as I can tell.













Posts with mentions or reviews of ClickHouse. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-07-19.
  • Garage: Open-Source Distributed Object Storage
    8 projects | | 19 Jul 2024
    Minio is fairly easy to setup locally or in CI.

    We use it for CI in ClickHouse, for example:

  • If you're using code on your site – like 100k are – remove it
    3 projects | | 26 Jun 2024
    PS. If you want to know about this dataset, check
  • A brief introduction to interval arithmetic
    3 projects | | 25 Jun 2024
    This is a good article!

    In ClickHouse, interval arithmetic is applied to index analysis. A sparse index consists of granules, and each granule is an interval of tuples in lexicographic order. This interval is decomposed into a union of hyperrectangles. Conditions such as comparisons, logic operators, and many other functions are evaluated on these hyperrectangles, yielding boolean intervals. Boolean intervals represent ternary logic (always true, always false, can be true or false). Interesting tricks include: applying functions that are monotonic on ranges (for example, the function "day of month" is monotonic as long as the month does not change), calculating function preimages on intervals, and even calculating preimages of n-ary functions, which is useful for space-filling curves, such as Morton or Hilbert curves.

    Check for more details:

    Or see examples, such as

  • Round Rects Are Everywhere
    3 projects | | 24 Jun 2024
    Round rectangles can look not round enough on low-DPI displays, and the colors can also be wrong due to incorrect averaging in non-linear color space. Example:

    It is similar to how subtle gradients look striped and dirty on many websites if displayed with only 24-bit color:

  • Ask HN: Why don't many platform data products offer self hosted?
    1 project | | 23 Jun 2024
    I would recommend to look at ClickHouse[0] which is Apache-License and is completely open-source. You can self-host it and it is one of the fastest growing OLAP database. It has good integrations with BI tools and it can be used for wide varieties of use cases. It is widely adopted[1]. I would recommend you to give it a try and validate it for your use-case.

    I work for ClickHouse and available for any queries or help you need.


  • Universal Data Migration: Using Slingdata to Transfer Data Between Databases
    2 projects | | 24 May 2024
    ClickHouse installed and running.
  • Simplified API Creation and Management: ClickHouse to APISIX Integration Without Code
    3 projects | | 22 May 2024
    In the world of data management and web services, creating and managing APIs can often be a complex and time-consuming task. However, with the right tools, this process can be significantly simplified. In this article, we will explore how to create APIs for fetching data from ClickHouse tables without writing any code and manage these APIs using APISIX. ClickHouse, a fast and open-source columnar database management system, provides an HTTP interface by default, enabling easy access to data. By integrating this with APISIX, an open-source API gateway, we can not only manage and log our APIs but also leverage a host of features provided by APISIX to enhance our API management capabilities.
  • The new APT 3.0 solver
    8 projects | | 14 May 2024
    I've made a library named "glibc-compatibility":

    When linking with this library, the resulting binary will not depend on the new symbol versions. It will run on glibc 2.4 and on systems as old as Ubuntu 8.04 and CentOS 5 even when built on the most modern system.

  • We Built a 19 PiB Logging Platform with ClickHouse and Saved Millions
    1 project | | 2 Apr 2024
    Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here:

    Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work:

  • Build time is a collective responsibility
    2 projects | | 24 Mar 2024
    In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.

    When these limits are reached, the CI stops working, and we have to remove the bloat:

    Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.

What are some alternatives?

When comparing platform and ClickHouse you can also consider the following projects:

perf-ninja - This is an online course where you can learn and master the skill of low-level performance analysis and tuning.

loki - Like Prometheus, but for logs.

DuckDB - DuckDB is an analytical in-process SQL database management system

Trino - Official repository of Trino, the distributed SQL query engine for big data, former

VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database

TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.

RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.

datafusion - Apache DataFusion SQL Query Engine

materialize - The data warehouse for operational workloads.

PostgreSQL - Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

TileDB - The Universal Storage Engine

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives

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