sgr (command line client for Splitgraph) and the splitgraph Python library (by splitgraph)

Sgr Alternatives

Similar projects and alternatives to sgr

  • metamask-extension

    :globe_with_meridians: :electric_plug: The MetaMask browser extension enables browsing Ethereum blockchain enabled websites

  • openpilot

    839 sgr VS openpilot

    openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.

  • 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.

    InfluxDB logo
  • Grafana

    379 sgr VS Grafana

    The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.

  • act

    145 sgr VS act

    Run your GitHub Actions locally 🚀

  • Mattermost

    142 sgr VS Mattermost

    Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..

  • sourcegraph

    Code AI platform with Code Search & Cody

  • Baserow

    45 sgr VS Baserow

    Open source no-code database and Airtable alternative. Create your own online database without technical experience. Performant with high volumes of data, can be self hosted and supports plugins (by bramw)

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

    WorkOS logo
  • sirix

    44 sgr VS sirix

    SirixDB is an an embeddable, bitemporal, append-only database system and event store, storing immutable lightweight snapshots. It keeps the full history of each resource. Every commit stores a space-efficient snapshot through structural sharing. It is log-structured and never overwrites data. SirixDB uses a novel page-level versioning approach.

  • parabol

    33 sgr VS parabol

    Free online agile retrospective meeting tool

  • Kedro

    29 sgr VS Kedro

    Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

  • grouparoo

    27 sgr VS grouparoo

    Discontinued 🦘 The Grouparoo Monorepo - open source customer data sync framework

  • go-mysql-server

    A MySQL-compatible relational database with a storage agnostic query engine. Implemented in pure Go.

  • goofys

    a high-performance, POSIX-ish Amazon S3 file system written in Go

  • odyssey

    Scalable PostgreSQL connection pooler

  • cargo-dist

    📦 shippable application packaging

  • haystack

    54 sgr VS haystack

    :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

  • Nightmare

    8 sgr VS Nightmare

    A high-level browser automation library.

  • dremio-oss

    Dremio - the missing link in modern data

  • pgbouncer-fast-switchover

    Adds query routing and rewriting extensions to pgbouncer

  • spqr

    Stateless Postgres Query Router.

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

    SaaSHub logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better sgr alternative or higher similarity.

sgr reviews and mentions

Posts with mentions or reviews of sgr. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-01.
  • Show HN: Loofi – Our AI-Powered SQL Query Builder
    1 project | | 21 May 2023
  • Release engineering is exhausting so here's cargo-dist
    12 projects | | 1 Feb 2023
    I wrote up the details of this in a PR [0] where I last dealt with it.


  • Ask HN: Serverless SQLite or Closest DX to Cloudflare D1?
    2 projects | | 2 Jan 2023
    This is the vision of what we're building at Splitgraph. [0] You might be most interested in our recent project Seafowl [1] which is an open-source analytical database optimized for running "at the edge," with cache-friendly semantics making it ideal for querying from Web applications. It's built in Rust using DataFusion and incorporates many of the lessons we've learned building the Data Delivery Network [2] for Splitgraph.




  • Postgres Auditing in 150 lines of SQL
    10 projects | | 9 Mar 2022
    You might like what we're doing with Splitgraph. Our command line tool (sgr) installs an audit log into Postgres to track changes [0]. Then `sgr commit` can write these changes to delta-compressed objects [1], where each object is a columnar fragment of data, addressable by the LTHash of rows added/deleted by the fragment, and attached to metadata describing its index [2].

    I haven't explored sirix before, but at first glance it looks like we have some similar ideas — thanks for sharing, I'm excited to learn more, especially about its application of ZFS.




  • The world of PostgreSQL wire compatibility
    3 projects | | 10 Feb 2022
    Shameless plug, but your list is missing Splitgraph [0] :)

    We’ve been based on Postgres from the beginning, and although the backend is a bit more complex at this point, we’ve kept the wire protocol intact. We’re also heavily invested in FDWs, not only for federated queries (e.g. querying data at Snowflake – btw, you might enjoy our blog post on achieving a 100x speedup with aggregation pushdown), but also for queries on warehoused data stored as Splitgraph images. By keeping Postgres compatibility as our guiding constraint, we’ve been able to build a lot of functionality on top of just a few simple abstractions. The result is something akin to a magic Postgres database – you can connect dozens of live sources to it using FDW plugins, or you can ingest from hundreds data sources using Airbyte connectors, ultimately storing the data as immutable Splitgraph images in object storage.

    As for the wire protocol, our implementation is heavily reliant on (a forked version of) PgBouncer. Basically, a query arrives, we parse it for references to tables (which look like Docker image tags), and the proxy layer performs whatever orchestration is necessary to satisfy the query. That could mean instantiating a foreign server to a saved connection, loading some data from object storage, or even lazily loading only the requisite data (we call this “layered querying” since it’s implemented similarly to AUFS). In the future, it could also mean delegating the query to a more specialized engine like Presto.

    Point is, by keeping the frontend intact, we’re able to retain compatibility with all Postgres clients, but we’re free to implement the backend in more scalable or domain specific ways. For example, we’re able to horizontally scale our query capacity by simply adding more “cache nodes” that perform the layered querying.

    We are definitely all-in on the Postgres wire protocol, and all the ecosystem compatibility that comes along with it. You can read our blog for more in depth discussions of this, but I don’t want to spam too many links here. :)



  • Scalable PostgreSQL Connection Pooler
    11 projects | | 12 Nov 2021
    We are building a solution for this problem at Splitgraph [0] – it sounds like we could probably help with your use case. You can get it to work yourself with our open source code [1], but our (private beta, upcoming public) SaaS service will put all your schemas on a more scalable “data delivery network,” which incidentally, happens to be implemented with PgBouncer + rewriting + ephemeral instances. In a local engine (just a Postgres DB managed by Splitgraph client to add extra stuff), there is no PgBouncer, but we use Foreign Data Wrappers to accomplish the same.

    On Splitgraph, every dataset – and every version of every dataset – has an address. Think of it like tagged Docker images. The address either points to an immutable “data image” (in which case we can optionally download objects required to resolve a query on-the-fly, although loading up-front is possible too) or to a live data source (in which case we proxy directly to it via FDW translation). This simple idea of _addressable data products_ goes a long way – for example, it means that computing a diff is now as simple as joining across two tables (one with the previous version, one with the new).

    Please excuse the Frankenstein marketing site – we’re in the midst of redesign / rework of info architecture while we build out our SaaS product.

    Feel free to reach out if you’ve got questions. And if you have a business case, we have spots available in our private pilot. My email is in my profile – mention HN :)


    [1] examples:

  • Ask HN: How to get compeitors to use our open source interop-prototcol?
    4 projects | | 4 Oct 2021
    Federated data sharing is the core use case of the magic Postgres database we’re building at Splitgraph [0]. We’d love to help you solve these problems! The ideas you’re describing are exactly what we want to achieve – data sharing should be as easy as changing a connection string in a SQL client. It sounds like your use case would be a good fit for what we’re building. If you’d like to learn more, please send me a note – email in profile.


  • Cloudera taken private for $5.3b, acquires Datacoral and Cazena
    2 projects | | 1 Jun 2021
    The data industry continues to hype this idea of “multi-cloud,” but then the “modern data stack” is centralized around a single warehouse and nobody sees any irony in that.

    The big bet we’re making at Splitgraph [0] is that the next wave of data engineering will take a more decentralized, “data mesh” type approach to enterprise architecture. “Data gravity” really does exist -expensive to move, in terms of both cost and operational complexity. So instead of bringing the data to the query, why not bring the query to the data? All we need for that is a set of read only credentials.

    Cloudera mentions they bought DataCoral to help with data integration and connectors. They’ve correctly identified the problem - data sprawl and fragmentation will inevitably grow - but I’m not sure they have the right solution.

    Data integration is important, but it’s a moving target, which is why it calls for a collaborative open source solution. This is why so many new startups, like AirByte most recently, are coalescing around the Singer taps that Stitch left behind after its acquisition by Talend.

    We also support using Singer taps to ingest data into versioned Splitgraph images [1], so we’re excited to see more collaboration on maintenance of taps. For us it’s a useful feature, but it should be just that — a feature. Is there really a need to replicate all of your data before you can even query it? Or would you rather experiment by directly querying its source?


    [1] unreleased and undocumented atm, but it does work. We’re hiring, especially on the frontend if you want to help build the web UI. See profile.

  • Google Dataset Search
    1 project | | 6 May 2021
    On the public DDN (, we enforce a (currently arbitrary) 10k row limit on responses. You can construct multiple queries using LIMIT and OFFSET, or you can run a local Splitgraph engine without a limit. We also have a private beta program if you want a managed or self-hosted deployment. And we are planning to ship some features for "export to csv" type use cases (potentially other output formats too).

    For live/external data, we proxy the query to the data source, so there is no theoretical data size limit except for any defined by the upstream.

    For snapshotted data, we store the data as fragments in object storage. Any size limit depends on the machine where Splitgraph's Postgres engine is running, and how you choose to materialize the data when downloading it from object storage. You can "check out" an entire image to materialize it locally, at which point it will be like any other Postgres schema. Or you can use "layered querying" which will return a result set while only materializing the fragments necessary to answer the query.

    Regarding ClickHouse, you could watch this presentation [0] my co-founder Artjoms gave at a recent ClickHouse meet-up on the topic of your question. We also have specific documentation for using the ClickHouse ODBC client with the DDN [1], as well as an example reference implementation. [2]




  • Ask HN: Who is hiring? (April 2021)
    21 projects | | 1 Apr 2021
    Splitgraph ( | Remote | Full-time

    Splitgraph is reshaping how organizations interact with data. We provide a unified interface to discover and query data. In practice, this means we're building a data catalog (a web app) and query layer (implemented with the Postgres wire protocol).

    We're a seed-stage, venture-funded startup hiring our initial team. The two co-founders are looking to grow the team by adding multiple engineers across the stack. This is an opportunity to make a big impact on an agile team while working closely with the founders.

    Splitgraph is a remote-first organization. The founders are based in the UK, and the company is incorporated in both USA and UK. Candidates are welcome to apply from any geography. We want to work with the most talented, thoughtful and productive engineers in the world.

    Open positions:

    * Senior Software Engineer - Frontend. Responsible for the web stack, mainly involving Typescript, React, Next.js, Postgraphile, etc.

    * Senior Software Engineer - Backend. Responsible for a variety of core services, using Python, Poetry, Postgres, C, Lua, and a ton of other technologies.

    Learn more & apply:

  • A note from our sponsor - SaaSHub | 20 Apr 2024
    SaaSHub helps you find the best software and product alternatives Learn more →


Basic sgr repo stats
6 months ago

SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives