![]() ![]() ![]() First, help you answer the first and important question: what is SQLite? You will have a brief overview of SQLite.Follow these 4-easy steps to get started with SQLite fast. You should go through this section if this is the first time you have worked with SQLite. SQLite is an open-source, zero-configuration, self-contained, stand-alone, transaction relational database engine designed to be embedded into an application. This SQLite tutorial is designed for developers who want to use SQLite as the back-end database or to use SQLite to manage structured data in applications including desktop, web, and mobile apps. In this tutorial, you will learn SQLite step by step through extensive hands-on practices. Install the sqlean.py package, which is a drop-in replacement for the default sqlite3 module: pip install sqlean.This SQLite tutorial teaches you everything you need to know to start using SQLite effectively. To resolve this issue, remove the extension from quarantine by running the following command in Terminal (replace /path/to/folder with an actual path to the folder containing the extension): xattr -d /path/to/folder/stats.dylibĪlso note that the “stock” SQLite CLI on macOS does not support extensions. macOS may disable unsigned binaries and prevent the extension from loading. Select median(value) from generate_series(1, 99) SQLiteStudio, SQLiteSpy or DBeaver: select load_extension('c:\Users\anton\sqlite\stats.dll') Sqlite> select median(value) from generate_series(1, 99) SQLite command-line interface (CLI, aka ‘sqlite3.exe’ on Windows): sqlite>. To load all extensions at once, use the single-file sqlean bundle. Then load it as described below.Įxamples use the stats extension you can specify any other supported extension. But you can also load them individually.įirst, download the extension. The easiest way to try out the extensions is to use the pre-bundled shell. Incubator extensions are also available for download. zorder: map multidimensional data to a single dimension.unionvtab: union similar tables into one.uint: natural string sorting and comparison.stats2 and stats3: additional math statistics functions.spellfix: search a large vocabulary for close matches.pearson: Pearson correlation coefficient between two data sets.math2: additional math functions and bit arithmetics.isodate: additional date and time functions.decimal, fcmp and ieee754: decimal and floating-point arithmetic.cron: match dates against cron patterns.compress and sqlar: compress / uncompress data.closure: navigate hierarchic tables with parent/child relationships.classifier: binary classifier via logistic regression.btreeinfo, memstat, recsize and stmt: various database introspection features.bloom: a fast way to tell if a value is already in a table.besttype: convert string value to numeric.Think of them as candidates for the standard library: They may be untested, poorly documented, too broad, too narrow, or without a well-thought API. These extensions haven’t yet made their way to the main set. There are precompiled binaries for Windows, Linix and macOS. The single-file sqlean bundle contains all extensions from the main set. stats: Math statistics - median, percentiles, etc.regexp: Pattern matching using regular expressions.fuzzy: Fuzzy string matching and phonetics.fileio: Reading and writing files and catalogs.define: User-defined functions and dynamic SQL.crypto: Hashing, encoding and decoding data.Think of them as the extended standard library for SQLite: They are tested, documented and organized into the domain modules with clear API. I plan to write in detail about each module in a separate article, but for now - here’s a brief overview. Something like a standard library in Python or Go, only for SQLite. So I started the sqlean project, which brings the extensions together, neatly packaged into domain modules, documented, tested, and built for Linux, Windows and macOS. As a result, there are a lot of SQLite extensions out there, but they are incomplete, inconsistent and scattered across the internet. Fortunately, the authors provided an extension mechanism, which allows doing almost anything. ![]() There are few built-in functions compared to PostgreSQL or Oracle. It’s a miniature embedded database, perfect for both exploratory data analysis and as a storage for small apps (I’ve blogged about that previously). ![]()
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