spyql
roapi
spyql | roapi | |
---|---|---|
23 | 24 | |
902 | 3,080 | |
- | 2.0% | |
0.0 | 6.9 | |
over 1 year ago | about 1 month ago | |
Jupyter Notebook | Rust | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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spyql
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Fq: Jq for Binary Formats
I prefer a SQL-like format. It’s not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!
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Command-line data analytics made easy with SPyQL
SPyQL documentation: spyql.readthedocs.io
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This Week In Python
spyql – Query data on the command line with SQL-like SELECTs powered by Python expressions
- Command-line data analytics made easy
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Jc – JSONifies the output of many CLI tools
This is great!
I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.
I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.
[1] https://github.com/dcmoura/spyql
- The fastest command-line tools for querying large JSON datasets
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Working with more than 10gb csv
You can import the data into a PostgreSQL/MySQL/SQLite/... database and then query the database. However, even with the right choice of indexes, it might take a while to run queries on a table with hundreds of millions of records. You can easily import your data to these databases with SpyQL: $ spyql "SELECT * FROM csv TO sql(table=my_table_name) | sqlite3 my.db" (you would need to create the table my_table_name before running the command).
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ClickHouse Cloud is now in Public Beta
https://github.com/dcmoura/spyql/blob/master/notebooks/json_...
And ClickHouse looks like a normal relational database - there is no need for multiple components for different tiers (like in Druid), no need for manual partitioning into "daily", "hourly" tables (like you do in Spark and Bigquery), no need for lambda architecture... It's refreshing how something can be both simple and fast.
- A SQLite extension for reading large files line-by-line
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I want to convert a large JSON file into Tabular Format.
I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql
roapi
- Full-fledged APIs for slowly moving datasets without writing code
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Tuql: Automatically create a GraphQL server from a SQLite database
If your use case is read-only I suggest taking a look at roapi[1]. It supports multiple read frontends (GraphQL, SQL, REST) and many backends like SQLite, JSON, google sheets, MySQL, etc.
[1] https://github.com/roapi/roapi
- Who is using AXUM in production?
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Ask HN: Best way to provide access to large data sets
For smaller datasets then anywhere up to a few mb which isn't so bad reasonable with an API but in theory for historic data it could be up to several gb. I've not seen datasette go that high (IIRC it's a 1000 row return limit by default).
That's what got me intrigued with Atlassians offering, as data lakes tend to be something internal to a company, not something I've ever seen offered as an interaction point to users.
I've also tested out roapi [1] which is nice if the data is in some structured format already (Parquet/JSON)
[1] https://github.com/roapi/roapi
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"thread 'main' panicked at 'no CA certificates found'", when running application in docker container
https://github.com/roapi/roapi/issues/103?
- Roapi 0.9 release adds support for all cloud storage providers
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SQLite-based databases on the Postgres protocol? Yes we can
Very cool and well executed project. Love the sprinkle of Rust in all the other companion projects as well :)
The ROAPI(https://github.com/roapi/roapi) project I built also happened to support a similar feature set, i.e. to expose sqlite through a variety of remote query interfaces including pg wire protocols, rest apis and graphqls.
- Using Rust to write a Data Pipeline. Thoughts. Musings.
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PostgREST – Serve a RESTful API from Any Postgres Database
> why not just accept SQL and cut out all the unnecessary mapping?
You might be interested in what we're building: Seafowl, a database designed for running analytical SQL queries straight from the user's browser, with HTTP CDN-friendly caching [0]. It's a second iteration of the Splitgraph DDN [1] which we built on top of PostgreSQL (Seafowl is much faster for this use case, since it's based on Apache DataFusion + Parquet).
The tradeoff for allowing the client to run any SQL vs a limited API is that PostgREST-style queries have a fairly predictable and low overhead, but aren't as powerful as fully-fledged SQL with aggregations, joins, window functions and CTEs, which have their uses in interactive dashboards to reduce the amount of data that has to be processed on the client.
There's also ROAPI [2] which is a read-only SQL API that you can deploy in front of a database / other data source (though in case of using databases as a data source, it's only for tables that fit in memory).
[0] https://seafowl.io/
[1] https://www.splitgraph.com/connect
[2] https://github.com/roapi/roapi
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Command-line data analytics made easy
It could be the NDJSON parser (DF source: [0]) or could be a variety of other factors. Looking at the ROAPI release archive [1], it doesn't ship with the definitive `columnq` binary from your comment, so it could also have something to do with compilation-time flags.
FWIW, we use the Parquet format with DataFusion and get very good speeds similar to DuckDB [2], e.g. 1.5s to run a more complex aggregation query `SELECT date_trunc('month', tpep_pickup_datetime) AS month, COUNT(*) AS total_trips, SUM(total_amount) FROM tripdata GROUP BY 1 ORDER BY 1 ASC)` on a 55M row subset of NY Taxi trip data.
[0]: https://github.com/apache/arrow-datafusion/blob/master/dataf...
[1]: https://github.com/roapi/roapi/releases/tag/roapi-v0.8.0
[2]: https://observablehq.com/@seafowl/benchmarks
What are some alternatives?
prql - PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
php-parquet - PHP implementation for reading and writing Apache Parquet files/streams. NOTICE: Please migrate to https://github.com/codename-hub/php-parquet.
malloy - Malloy is an experimental language for describing data relationships and transformations.
qframe - Immutable data frame for Go
tresql - Shorthand SQL/JDBC wrapper language, providing nested results as JSON and more
materialize - The data warehouse for operational workloads.
Preql - An interpreted relational query language that compiles to SQL.
delta-rs - A native Rust library for Delta Lake, with bindings into Python
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
fluvio - Lean and mean distributed stream processing system written in rust and web assembly.
pxi - 🧚 pxi (pixie) is a small, fast, and magical command-line data processor similar to jq, mlr, and awk.
datasette - An open source multi-tool for exploring and publishing data