sqlparse
Apache Calcite
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sqlparse | Apache Calcite | |
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7 | 28 | |
3,557 | 4,307 | |
- | 2.1% | |
8.2 | 9.0 | |
1 day ago | 7 days ago | |
Python | Java | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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sqlparse
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Show HN: Databasediagram.com – Private, Text to Entity-Relationship Diagram Tool
Suggest checking out the sqlparse library for a way to do the different flavours without needing to address each case directly: https://github.com/andialbrecht/sqlparse
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Data Load Diagram
Gotcha, since we haven't actually written all of this yet I don't have any useful code snippets to share but we've discussed tackling the problem internally using something like sqlparse. You'd need to identify the relevant sql chunks, parse them for table dependency information and then create the relevant entities in whichever data lineage tool you were using.
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This Week In Python
sqlparse – A non-validating SQL parser module for Python
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Open Source SQL Parsers
Regular expressions is a popular approach to extract information from SQL statements. However, regular expressions quickly become too complex to handle common features like WITH, sub-queries, windows clauses, aliases and quotes. sqlparse is a popular python package that uses regular expressions to parse SQL.
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Automated SQL formatting checks
This one is not bad: https://github.com/andialbrecht/sqlparse.
- Let's write a compiler, part 5: A code generator
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?
- Want to contribute more to open source projects.
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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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Open Source SQL Parsers
There are multiple projects that maintain parsers for popular open source databases like MySQL and Postgres. For other open source databases, the grammar can be extracted from the open-source project. For commercial databases, the only option is to reverse engineer the complete grammar. There are SQL parser/optimizer platforms like Apache Calcite that help to reduce the effort to implement the SQL dialect of your choice.
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Does Java have an open source package that can execute SQL on txt/csv?
Yes. Apache Calcite can do that.
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Memoization in Cost-based Optimizers
You may find a similar design in many production-grade heuristic optimizers. In our previous blog post about Presto, we discussed the Memo class that manages such references. In Apache Calcite, the heuristic optimizer HepPlanner models node references through the class HepRelVertex.
What are some alternatives?
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
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
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
zetasql - ZetaSQL - Analyzer Framework for SQL
pyparsing - Python library for creating PEG parsers [Moved to: https://github.com/pyparsing/pyparsing]
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
PLY - Python Lex-Yacc
Apache Drill - Apache Drill is a distributed MPP query layer for self describing data
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.