duckdb

name: duckdb description: DuckDB — the in-process, columnar, vectorized OLAP SQL engine shipped as a single zero-dependency binary ("SQLite for analytics"). Use when querying Parquet/CSV/JSON files directly with SQL and no load step, doing in-process analytical/OLAP queries and aggregations, using the duckdb CLI shell (interactive REPL or -c/-json one-shots for scripts and agents), writing "friendly SQL" (FROM-first, SELECT * EXCLUDE, GROUP BY ALL, SUMMARIZE, PIVOT), converting CSV↔Parquet↔JSON with COPY, reading remote data over HTTP/S3 via the httpfs extension, or ATTACH-ing a live Postgres/MySQL/SQLite database. Triggers on mentions of duckdb, the duckdb command, .duckdb files, replacement scans, read_parquet/read_csv, httpfs/CREATE SECRET, or "embedded analytics database". This is the DuckDB CLI and SQL dialect, NOT a generic SQL tutorial and NOT the DuckDB client libraries (Python/Node/Java — defer those to duckdb.org).

DuckDB - In-Process Analytical SQL Engine

Overview

DuckDB is "SQLite for analytics": an in-process (embedded — no server, no daemon, no network) OLAP database that runs inside the duckdb process itself. It is columnar and vectorized (it processes batches of column values rather than row-at-a-time), which makes aggregations and scans over large tables fast. A database is a single file (my.db) or purely in-memory (:memory:); it is ACID (MVCC transactions); and it ships as a single zero-dependency binary. Its superpower for CLI and agent work: files are tables — you can SELECT straight out of CSV / Parquet / JSON (local, globbed, or over HTTP/S3) with no import step.

Key characteristics:

• In-process / embedded — runs in the duckdb process; no server to start, no port to open
• Columnar + vectorized OLAP — built for analytical scans and aggregations, not point lookups
• Files are tables — FROM 'data.parquet' queries the file directly (replacement scans)
• Single binary, zero deps — one executable is the whole engine; :memory: or a single .db file
• ACID — MVCC transactions, single-file storage; one read-write process at a time
• Postgres-ish dialect + "friendly SQL" — familiar SQL plus ergonomic extras (FROM-first, EXCLUDE, GROUP BY ALL, SUMMARIZE)

Disambiguation: This skill documents the duckdb CLI and its SQL dialect — the shell, output modes, replacement scans, COPY, ATTACH, extensions, and friendly-SQL syntax. It is not a generic SQL course, and it does not cover the DuckDB client libraries (Python, Node.js, Java, R, Go, etc.) — for embedding DuckDB in an application, use the API docs at https://duckdb.org/docs/.

When to Use This Skill

• vs SQLite: same "embedded single-file" feel, but DuckDB is columnar/OLAP where SQLite is row-store/OLTP. DuckDB can read SQLite files via the sqlite_scanner extension.
• vs Postgres: no server, no roles, no network — but DuckDB speaks a Postgres-compatible-ish dialect plus friendly-SQL extras, and can ATTACH a live Postgres database.

Prerequisites

CRITICAL: Before proceeding, verify DuckDB is installed and check the version:

duckdb -version        # prints e.g. "v1.3.2 (Ossivalis) 0b83e5d2f6"

Version note: This skill is documented against the DuckDB CLI surface as of v1.3.x "Ossivalis" for the core examples; the current released line is 1.5.x (1.4 is the LTS). Long-standing SQL and CLI features (everything stable at or before the 1.0.0 GA) are "bedrock" and shown unannotated. Features added in a specific release are tagged inline as (duckdb vX.Y+) only where a duckdb.org release blog sources them — see references/version-features.md for the full feature → version map with citations. Always confirm on the running build with duckdb -version.

If DuckDB is not installed: it is a single self-contained binary with no dependencies.

Install

DuckDB is one file, no deps — install the binary and you have the whole engine.

# macOS (Homebrew)
brew install duckdb

# Linux/macOS — official install script (installs to ~/.duckdb/cli, adds to PATH)
curl https://install.duckdb.org | sh

# Or download a static binary from https://duckdb.org/docs/installation
# Verify:
duckdb -version          # → v1.3.2 (Ossivalis) 0b83e5d2f6  (example)

The duckdb CLI at a Glance

These six invocations cover ~90% of CLI usage:

duckdb                          # in-memory scratch REPL (nothing is persisted)
duckdb mydb.db                  # open/create a persistent database file
duckdb -c "FROM 'data.csv'"     # run one SQL command and exit (ideal for scripts/agents)
duckdb -json -c "SELECT 42"     # machine-readable output (best for parsing)
echo "SELECT 1" | duckdb        # pipe SQL in via stdin
duckdb data.parquet -c 'FROM data LIMIT 5'   # open a data FILE directly as the DB (duckdb v1.3+)

Usage: duckdb [OPTIONS] [FILENAME] [SQL]. FILENAME is a DuckDB database file (created if absent); omit it (or pass :memory:) for an in-memory database. A trailing SQL argument runs and then drops into the REPL.

Most useful flags (full list in references/cli.md):

Output modes (set with a flag or .mode MODE): duckbox (default pretty box), json (array of row objects — best for agents/scripts), csv, markdown, table, line, jsonlines, insert, and more.

Dot-commands (SQLite-style, in the REPL): .mode, .tables, .schema, .open FILE, .read FILE, .output FILE, .timer on, .maxrows N, .help, .quit. Full list in references/cli.md.

Core Workflows

1. Query a file without importing it

FROM 'path' auto-detects the format by extension and content — no CREATE TABLE, no load step.

duckdb -c "FROM 'sales.csv' WHERE region = 'EU' LIMIT 10"
duckdb -c "SELECT region, sum(amount) FROM 'sales.parquet' GROUP BY ALL"
duckdb -c "FROM 'logs/**/*.json'"          # recursive glob across many files

Note: 'single quotes' = a string literal / file path; "double quotes" = an identifier (column/table). So FROM 'x.csv' reads the file; FROM x references a table.

2. Persistent database vs. in-memory

# In-memory: fast scratchpad, everything is GONE on exit
duckdb -c "CREATE TABLE t AS FROM 'raw.parquet'; SELECT count(*) FROM t"

# Persistent: open a file path to keep the data
duckdb shop.db -c "CREATE TABLE orders AS FROM 'raw.parquet'"
duckdb shop.db -c "SELECT count(*) FROM orders"   # data is still there next run

Open the REPL on a file (duckdb shop.db) and use .tables / .schema to inspect it.

3. Convert formats with COPY … TO

# CSV → Parquet (zstd-compressed)
duckdb -c "COPY (FROM 'in.csv') TO 'out.parquet' (FORMAT parquet, COMPRESSION zstd)"

# Table → CSV with a header
duckdb shop.db -c "COPY orders TO 'orders.csv' (HEADER, DELIMITER ',')"

# Partitioned (Hive-layout) write
duckdb -c "COPY (FROM 'events.parquet') TO 'out_dir' (FORMAT parquet, PARTITION_BY (year, month))"

Supported formats: parquet, csv, json. See references/data-io.md for all options, plus EXPORT DATABASE / IMPORT DATABASE for whole-database snapshots.

4. Explore unknown data with friendly SQL

# Per-column stats: min/max/approx_unique/avg/std/quartiles/null%
duckdb -c "SUMMARIZE FROM 'mystery.parquet'"

# Column names + types
duckdb -c "DESCRIBE FROM 'mystery.parquet'"

# Drop noisy columns from a star-select; FROM-first is valid too
duckdb -c "FROM 'mystery.parquet' SELECT * EXCLUDE (internal_id, raw_blob) LIMIT 20"

Friendly-SQL highlights (all bedrock): FROM-first (FROM t SELECT … or bare FROM t), SELECT * EXCLUDE (…) / REPLACE (…), COLUMNS('regex'), GROUP BY ALL, ORDER BY ALL, QUALIFY, PIVOT / UNPIVOT, USING SAMPLE, and UNION [ALL] BY NAME. Full reference: references/sql-dialect.md.

5. Query remote data over HTTP / S3 (httpfs)

duckdb <<'SQL'
INSTALL httpfs; LOAD httpfs;          -- most core extensions autoload, but this is explicit
CREATE SECRET s3 (TYPE s3, PROVIDER credential_chain, REGION 'us-east-1');
FROM 's3://my-bucket/year=2026/*.parquet' LIMIT 100;
SQL

CREATE SECRET is the modern auth path (it replaces the old SET s3_* settings); use PROVIDER credential_chain to pick up the AWS default credential chain, or pass KEY_ID/SECRET explicitly. You can also read plain https://…/file.parquet URLs directly.

6. ATTACH a live Postgres / SQLite database

# Read a SQLite file as a catalog (sqlite_scanner extension)
duckdb -c "ATTACH 'app.db' AS s (TYPE sqlite); FROM s.users LIMIT 10"

# Query a live Postgres database (postgres extension)
duckdb <<'SQL'
ATTACH 'host=db.example.com dbname=app user=me' AS pg (TYPE postgres);
SELECT count(*) FROM pg.public.orders;
-- copy a Postgres table into a local DuckDB table:
CREATE TABLE local_orders AS FROM pg.public.orders;
SQL

TYPE sqlite | postgres | mysql selects the scanner (each autoloads its extension). Use USE pg; to set the default catalog and DETACH pg; when done. More in references/data-io.md.

Non-Interactive / Agent Usage

DuckDB is excellent for scripts and agents — prefer one-shot, machine-readable invocations over the REPL:

# One-shot, JSON out (the safest format to parse)
duckdb -json -c "SELECT count(*) AS n FROM 'events.parquet'"

# Multi-statement via heredoc
duckdb mydb.db <<'SQL'
CREATE TABLE t AS FROM 'in.csv';
COPY t TO 'out.parquet';
SQL

# Read-only guard when opening an existing database file (untrusted queries)
duckdb -readonly mydb.db -c "FROM orders LIMIT 100"
# (Reading external files like Parquet/CSV/JSON needs NO -readonly — and -readonly
#  requires a file DB; it errors against the default :memory: database.)

# CSV out (no header) piped to another tool
duckdb -csv -noheader -c "SELECT id FROM t" | sort -u

Agent tips: prefer -json; use -readonly when opening an existing file database to guard against writes (it errors against :memory:, and isn't needed just to read external files); use -c/-f/heredoc rather than the interactive REPL; use :memory: when no persistence is needed; combine with -bail so the exit code is non-zero on error.

Quick Reference

Troubleshooting

• :memory: loses everything on exit — open a file path (duckdb my.db) to persist data.
• -c runs one statement set then exits — for multiple statements, separate with ;, or use a heredoc, -f script.sql, or .read script.sql.
• Single vs double quotes — '…' is a string literal / file path; "…" is an identifier (column/table). FROM 'x.csv' (read file) vs FROM x (table named x).
• IO Error: No files found that match the pattern … — it's a path/glob problem, not SQL. Check the working directory and the glob (*.parquet vs **/*.parquet).
• Catalog Error: … does not exist — usually an unloaded extension; run INSTALL ext; LOAD ext; (though most core extensions autoload on first use). Inspect with SELECT extension_name, installed, loaded FROM duckdb_extensions();.
• A newer feature errors out (Parser/Binder/Catalog Error) — your build may be too old. Check duckdb -version and see references/version-features.md for the minimum version of each feature. MERGE INTO (duckdb v1.4+), VARIANT (duckdb v1.5+), and ATTACH … (COMPRESS …) (duckdb v1.4+), for example, won't run on 1.3.x. (Database encryption is a documented 1.4 feature, but the ENCRYPTION_KEY option already works in 1.3.x.)
• Lambda arrow deprecation — x -> x+1 warns on newer builds (duckdb v1.5+); prefer the Python-style lambda x: x+1 (duckdb v1.3+).
• One writer at a time — a DB file opened read-write by one process blocks others. Use -readonly to allow concurrent readers.

References

For exhaustive detail, see the bundled reference files:

• references/cli.md — the duckdb shell: every command-line flag, all output modes, the full dot-command list, and non-interactive/agent patterns.
• references/sql-dialect.md — "friendly SQL": FROM-first, EXCLUDE/ REPLACE/COLUMNS, GROUP BY ALL, QUALIFY, PIVOT/UNPIVOT, joins (ASOF/POSITIONAL), nested types, lambdas, and DDL/DML ergonomics.
• references/data-io.md — replacement scans, read_* readers, COPY, EXPORT/IMPORT DATABASE, ATTACH, extensions, and httpfs/S3 secrets.
• references/version-features.md — feature → minimum-version map with duckdb.org release-blog citations (what's bedrock vs. added in 1.1–1.5).

Resources

• Help: duckdb -help, .help in the REPL, .help -all
• Official docs: https://duckdb.org/docs/
• Friendly SQL: https://duckdb.org/docs/stable/sql/dialect/friendly_sql
• Extensions: https://duckdb.org/docs/stable/extensions/overview