zetasql
Apache Calcite
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zetasql | Apache Calcite | |
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15 | 28 | |
2,127 | 4,352 | |
1.5% | 1.8% | |
0.0 | 9.0 | |
about 2 months ago | about 15 hours ago | |
C++ | Java | |
Apache License 2.0 | Apache License 2.0 |
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zetasql
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Mangle, a programming language for deductive database programming
There are even table-valued functions.
These things are not widespread, and differ by implementation, and the way these are used by clients are copy-and-paste. Something as thoughtful as ZetaSQL https://github.com/google/zetasql does not have mechanisms for structuring (modules, packages, interfaces). SQL will not, cannot evolve into such a direction (or, anything that evolves, will not be recognizable as SQL).
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goccy/bigquery-emulator: BigQuery emulator server implemented in Go
Hi, I develop a BigQuery emulator ( https://github.com/goccy/bigquery-emulator ) from early 2022. It is written in Go, but can be used from bq command line tool and other language's (e.g. Python ) client SDK after installing docker image or released binary. It currently supports over 200 of the nearly 330 standard functions in BigQuery and all data types except GEOGRAPHY ( see https://github.com/goccy/go-zetasqlite#status for details ). ZetaSQL ( https://github.com/google/zetasql ) is used to parse and analyze queries.
- ZetaSQL – Analyzer Framework for SQL
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ZetaSQL - Question about using local service
We are using a Python client binding for ZetaSQL GRPC local service in our application to analyze statements and extract referenced tables and output columns.
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Parsing SQL
If you don't want to do it yourself, there's this:
https://github.com/google/zetasql
Parsing is huge but it's amazing how small a part of the job it is. This library isn't even the half of it.
- SQLGlot: SQL parser, transpiler, optimizer – translate to Presto, Spark, Hive
- ZetaSQL - Analyzer Framework for SQL
- ZetaSQL
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New PostgreSQL Interface for Cloud Spanner
I mean the postgres parser (and semantic changes) for ZetaSQL. The zetasql parser is in a file called zetasql/parser/bison_parser.y, I strongly suspect they now have a file called something like zetasql/pgparser/bison_parser.y as well (and much more pervasive changes to support the deeper differences in the dialects).
This is the lexical structure and syntax docs for the new postgres inteface to cloud spanner:
https://cloud.google.com/spanner/docs/postgresql/lexical
And this is the zetasql lexical structure and syntax docs:
https://github.com/google/zetasql/blob/master/docs/lexical.m...
Notice that the new PG docs are an edit of the Zeta ones - evidence that my hypothesis is correct.
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Open Source SQL Parsers
zetasql implements BigQuery, Spanner, and Dataflow dialects.
Apache Calcite
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Data diffs: Algorithms for explaining what changed in a dataset (2022)
> Make diff work on more than just SQLite.
Another way of doing this that I've been wanting to do for a while is to implement the DIFF operator in Apache Calcite[0]. Using Calcite, DIFF could be implemented as rewrite rules to generate the appropriate SQL to be directly executed against the database or the DIFF operator can be implemented outside of the database (which the original paper shows is more efficient).
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Apache Baremaps: online maps toolkit
Yes, planetiler rocks and the memory mapped collections enabled us to remove our dependency to rocksdb.
From my perspective, planetiler started as an effort to generate vector tiles from the OpenMapTile schema as fast as possible (pbf -> mvt). By contrast, Baremaps started as an effort to create a new schema and style from the ground up. In this regard, having a database (pbf -> db <- mvt) enables to live reload changes made in the configuration files. The database has a cost, but also comes with additional advantages (updates, dynamic data, generation of tiles at zoom levels 16+, etc.).
That being said, I think the two projects overlap and I hope we will find opportunities to collaborate in the future. For instance, whereas PostgreSQL is still required in Baremaps, I recently ported a lot of the ST_ function of Postgis to Apache Calcite with the intent to execute SQL on fast memory mapped collection.
https://github.com/apache/calcite/blob/main/core/src/main/ja...
A planet wide import in Postgis currently takes about 4 hours with the COPY API (easy to parallelize) followed by about 12 hours of simplification in Postgis (not easy to parallelize). I will try to publish a detailed benchmark in the future.
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How to manipulate SQL string programmatically?
Use a SQL Parser like sqlglot or Apache Calcite to compile user's query into an AST.
- Can SQL be used without an RDBMS?
- Apache Calcite
- Want to contribute more to open source projects.
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CITIC Industrial Cloud — Apache ShardingSphere Enterprise Applications
The SQL Federation engine contains processes such as SQL Parser, SQL Binder, SQL Optimizer, Data Fetcher and Operator Calculator, suitable for dealing with co-related queries and subqueries cross multiple database instances. At the underlying layer, it uses Calcite to implement RBO (Rule Based Optimizer) and CBO (Cost Based Optimizer) based on relational algebra, and query the results through the optimal execution plan.
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Postgres wire compatible SQLite proxy
Awesome to see work in the DB wire compatible space. On the MySQL side, there was MySQL Proxy (https://github.com/mysql/mysql-proxy), which was scriptable with Lua, with which you could create your own MySQL wire compatible connections. Unfortunately it appears to have been abandoned by Oracle and IIRC doesn't work with 5.7 and beyond. I used it in the past to hack together a MySQL wire adapter for Interana (https://scuba.io/).
I guess these days the best approach for connecting arbitrary data sources to existing drivers, at least for OLAP, is Apache Calcite (https://calcite.apache.org/). Unfortunately that feels a little more involved.
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Launch HN: Hydra (YC W22) – Query Any Database via Postgres
For anyone interested, Apache Calcite[0] is an open source data management framework which seems to do many of the same things that Hydra claims to do, but taking a different approach. Operating as a Java library, Calcite contains "adapters" to many different data sources from existing JDBC connectors to Elasticsearch to Cassandra. All of these different data sources can be joined together as desired. Calcite also has it's own optimizer which is able to push down relevant parts of the query to the different data sources. However, you get full SQL on data sources which don't support it, with Calcite executing the remaining bits itself.
Unfortunately, I would not be too surprised if Calcite was found to be less performance-optimized than Hydra. That said, there are users of Calcite at Google, Uber, Spotify, and others who have made great use of various parts of the framework.
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Anyone know of any software that can help in designing then outputting to various database
Abstraction Layer - You can use something like Calcite to abstract out your data storage. https://calcite.apache.org/
What are some alternatives?
sqlparse - A non-validating SQL parser module for Python
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
JSqlParser - JSqlParser parses an SQL statement and translate it into a hierarchy of Java classes. The generated hierarchy can be navigated using the Visitor Pattern
ANTLR - ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.
Presto - The official home of the Presto distributed SQL query engine for big data
pgsql-parser - PostgreSQL Query Parser for Node.js
sqlite-parser - JavaScript implentation of SQLite 3 query parser
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
sqlglot - Python SQL Parser and Transpiler
Apache Drill - Apache Drill is a distributed MPP query layer for self describing data