go-sqlite3-stdlib
materialize
go-sqlite3-stdlib | materialize | |
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6 | 117 | |
123 | 5,585 | |
0.0% | 0.8% | |
0.0 | 10.0 | |
9 months ago | 5 days ago | |
Go | Rust | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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go-sqlite3-stdlib
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SQLite: Past, Present, and Future
Adding user-defined functions to SQLite is not difficult, and the mechanism is quite flexible. You can create extensions and load them when you create the SQLite connection to have the functions available in queries. I wrote a blog post explaining how to do that using Rust, and the example is precisely a `regex_extract` function [0].
If you need them, you also have a "stdlib" implemented for Go [1] and a pretty extensive collection of extensions [2]
[0]: https://ricardoanderegg.com/posts/extending-sqlite-with-rust...
[1]: https://github.com/multiprocessio/go-sqlite3-stdlib
[2]: https://github.com/nalgeon/sqlean
- SQLite has pretty limited builtin functions
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OctoSQL allows you to join data from different sources using SQL
OctoSQL is an awesome project and Kuba has a lot of great experience to share from building this project I'm excited to learn from.
And while building a custom database engine does allow you to do pretty quick queries, there are a few issues.
First, the SQL implemented is nonstandard. As I was looking for documentation and it pointed me to `SELECT * FROM docs.functions fs`. I tried to count the number of functions but octosql crashed (a Go panic) when I ran `SELECT count(1) FROM docs.functions fs` and `SELECT count() FROM docs.functions fs` which is what I lazily do in standard SQL databases. (`SELECT count(fs.name) FROM docs.function fs` worked.)
This kind of thing will keep happening because this project just doesn't have as much resources today as SQLite, Postgres, DuckDB, etc. It will support a limited subset of SQL.
Second, the standard library seems pretty small. When I counted the builtin functions there were only 29. Now this is an easy thing to rectify over time but just noting about the state today.
And third this project only has builtin support for querying CSV and JSON files. Again this could be easy to rectify over time but just mentioning the state today.
octosql is a great project but there are also different ways to do the same thing.
I build dsq [0] which runs all queries through SQLite so it avoids point 1. It has access to SQLite's standard builtin functions plus* a battery of extra statistic aggregation, string manipulation, url manipulation, date manipulation, hashing, and math functions custom built to help this kind of interactive querying developers commonly do [1].
And dsq supports not just CSV and JSON but parquet, excel, ODS, ORC, YAML, TSV, and Apache and nginx logs.
A downside to dsq is that it is slower for large files (say over 10GB) when you only want a few columns whereas octosql does better in some of those cases. I'm hoping to improve this over time by adding a SQL filtering frontend to dsq but in all cases dsq will ultimately use SQLite as the query engine.
You can find more info about similar projects in octosql's Benchmark section but I also have a comparison section in dsq [2] and an extension of the octosql benchmark with different set of tools [3] including duckdb.
Everyone should check out duckdb. :)
[0] https://github.com/multiprocessio/dsq
[1] https://github.com/multiprocessio/go-sqlite3-stdlib
[2] https://github.com/multiprocessio/dsq#comparisons
[3] https://github.com/multiprocessio/dsq#benchmark
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One year as a solo dev building open-source data tools without funding
Hey Kuba!
> Especially on the community building aspect, it's really impressive that you've been able to spark so many communities on various platforms (Reddit, GitHub, Discord, etc.)!
Yeah it's been so cool to see so many people come together, hobbyists and professionals.
> On a more technical note, since dsq is based on the "load it into SQLite and query it from there" architecture, have you considered integrating with the plugin ecosystems of other existing projects based on that same architecture, like Datasette[0]? It seems like a way to add a lot of value to your tools without much work.
Interesting idea! I haven't looked into Datasette too much. And I haven't thought about plugins too much either. The most I've done is extend the SQLite standard library [0] and I hope to continue growing that. I'd be curious to hear what specifically people like from Datasette they'd like to see in dsq.
> On a more commercial note, overall I think tools like this are very hard to monetize, because right now they're just a fairly niche use case, between - as you mentioned - full blown data analytics platforms and observability query systems, as well as standard unix tools. Especially since if you need the analytics a lot, you'll probably have time to integrate it into your preferred analytics solution (like BigQuery). Do you have any thoughts on that?
My idea was always to focus on smaller and less mature organizations, probably ones that have been around for 10+ years. They aren't using BigQuery, they prefer to host everything themselves, and they don't yet realize there are tools like DataStation that they can easily run to make analytics easier.
I've worked at a bunch of companies like this so I know the market exists. Actually I have been surprised how many people outside of this market showed up in the DataStation community. I've seen Googlers, MS-ers, modern startups, data science teams show up interested in DataStation compared to what they're already using.
For me it's just been a matter of time (and funding) to build out the product to serve these communities commercially as a SaaS or enterprise product.
[0] https://github.com/multiprocessio/go-sqlite3-stdlib
- Show HN: A standard library for mattn/go-sqlite3
- A standard library for mattn/go-sqlite3 including best-effort date parsing, url parsing, math/string functions, and stats aggregation functions
materialize
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Ask HN: How Can I Make My Front End React to Database Changes in Real-Time?
[2] https://materialize.com/
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize, DeltaStream, and TimePlus. While they each have distinct commercial and technical approaches, their overarching goal remains consistent: to offer users cloud-based streaming database services.
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Proton, a fast and lightweight alternative to Apache Flink
> Materialize no longer provide the latest code as an open-source software that you can download and try. It turned from a single binary design to cloud-only micro-service
Materialize CTO here. Just wanted to clarify that Materialize has always been source available, not OSS. Since our initial release in 2020, we've been licensed under the Business Source License (BSL), like MariaDB and CockroachDB. Under the BSL, each release does eventually transition to Apache 2.0, four years after its initial release.
Our core codebase is absolutely still publicly available on GitHub [0], and our developer guide for building and running Materialize on your own machine is still public [1].
It is true that we substantially rearchitected Materialize in 2022 to be more "cloud-native". Our new cloud offering offers horizontal scalability and fault tolerance—our two most requested features in the single-binary days. I wouldn't call the new architecture a microservices design though! There are only 2-3 services, each quite substantial, in the new architecture (loosely: a compute service, an orchestration service, and, soon, a load balancing service).
We do push folks to sign up for a free trial of our hosted cloud offering [2] these days, rather than trying to start off by running things locally, as we generally want folks' first impression of Materialize to be of the version that we support for production use cases. A all-in-one single machine Docker image does still exist, if you know where to look, but it's very much use-at-your-own-risk, and we don't recommend using it for anything serious, but it's there to support e.g. academic work that wants to evaluate Materialize's capabilities to incrementally maintain recursive SQL queries.
If folks have questions about Materialize, we've got a lively community Slack [3] where you can connect directly with our product and engineering teams.
[0]: https://github.com/MaterializeInc/materialize/tree/main
- What I Talk About When I Talk About Query Optimizer (Part 1): IR Design
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We Built a Streaming SQL Engine
Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.
https://github.com/timelydataflow/differential-dataflow
https://materialize.com/
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Ask HN: Who is hiring? (October 2023)
Materialize | Full-Time | NYC Office or Remote | https://materialize.com
Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.
Materialize is the operational data warehouse built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.
Senior/Staff Product Manager - https://grnh.se/69754ebf4us
Senior Frontend Engineer - https://grnh.se/7010bdb64us
===
Investors include Redpoint, Lightspeed and Kleiner Perkins.
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Ask HN: Who is hiring? (June 2023)
Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/
You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.
That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.
Engineering Manager, Compute - https://grnh.se/4e14099f4us
Senior Product Manager - https://grnh.se/587c36804us
VP of Marketing - https://grnh.se/9caac4b04us
- What are your favorite tools or components in the Kafka ecosystem?
- Ask HN: Who is hiring? (May 2023)
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Dozer: A scalable Real-Time Data APIs backend written in Rust
How does it compare to https://materialize.com/ ?
What are some alternatives?
sqlite-past-present-future - Performance evaluation and optimization of SQLite
ClickHouse - ClickHouse® is a free analytics DBMS for big data
octosql-plugin-postgres
risingwave - SQL stream processing, analytics, and management. We decouple storage and compute to offer speedy bootstrapping, dynamic scaling, time-travel queries, and efficient joins.
sqlite-plus - The ultimate set of SQLite extensions
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.
octosql-plugin-random_data - OctoSQL plugin serving random data
rust-kafka-101 - Getting started with Rust and Kafka
mycelite - Mycelite is a SQLite extension that allows you to synchronize changes from one instance of SQLite to another.
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
cargo-semver-checks - Scan your Rust crate for semver violations.
scryer-prolog - A modern Prolog implementation written mostly in Rust.