Stream-Framework VS sgr

I have looked into a getstream.io integration, however it seems that the Ruby SDK is really treated as a second class citizen. There's bugs with the documented API (I'm having issues even creating users and querying users), the usage of the gem is low and there is an open issue since May that no one has even looked at, which doesn't give me hope for long term support.

An ultralight social media app with no dependencies that can run on shared web hosting. It's an API like Getstream, so F/E is up to you. I've had a fork of it in production for 2.5 years on a subscription site that generates a small income.

Now, I'm seeking your advice and opinions. If you have experience using Stream or MirrorFly for chat implementation, I'd greatly appreciate any insights you can provide. Here are some questions I have:

Think about the edge that you can have over thousands other people looking for a job like you. One of the ways to do it – tailor your (even small) experience to the company you are applying to. E.g. Let's take a company like Stream that have an open-source Swift SDK, try to contribute to their SDK, maybe close some `good-first-issue`s here and there, do some documentation improvements, enrich their example app. So that when you feel like you are ready to knock their door – you already have an edge over others – you don't need onboarding (because you already know most of their codebase, you even completed your first few tasks while yet not being employed!)

This is absolutely absurd. I mean I 100% believe it happens, it's just absurd that the Tier 1 admin bot is this bad.

I too firmly believe the tier 1 admins are just this program - https://getstream.io/ And reddit doesn't want to admit that humans are not actually reviewing things

Really depends on so many variables, and comes down to knowing the audience. I built a lightweight social network api platform to do a lot of what getstream.io and OSSN does, but the way users engaged with it was nothing like what I expected. One use case, for example, would have been mostly satisfied with WordPress and Mailchimp. Forums are very familiar to people and still used by a lot of "I don't do social media" types. At the risk of making a sweeping generalization, if you have an older target audience they will be happy with a forum. If you have a younger target audience they will prefer the sequential post / react style of interacting, like Discord or Instagram.

I wrote up the details of this in a PR [0] where I last dealt with it.

[0] https://github.com/splitgraph/sgr/pull/656

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.

[0] https://www.splitgraph.com

[1] https://seafowl.io

[2] https://www.splitgraph.com/connect

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.

[0] https://www.splitgraph.com/docs/working-with-data/tracking-c...

[1] https://www.splitgraph.com/docs/concepts/objects

[2] https://github.com/splitgraph/splitgraph/blob/master/splitgr...

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. :)

[0] https://www.splitgraph.com

[1] https://www.splitgraph.com/blog/postgresql-fdw-aggregation-p...

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 :)

[0] https://www.splitgraph.com/connect

[1] examples: https://github.com/splitgraph/splitgraph/tree/master/example...

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.

[0] https://www.splitgraph.com

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?

[0] https://www.splitgraph.com

[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.

On the public DDN (data.splitgraph.com:5432), 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]

[0] https://www.youtube.com/watch?v=44CDs7hJTho

[1] https://www.splitgraph.com/connect

[2] https://github.com/splitgraph/splitgraph/tree/master/example...

Splitgraph (https://www.splitgraph.com) | 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: https://www.notion.so/splitgraph/Splitgraph-is-Hiring-25b421...