Install
$ agentstack add skill-redpanda-data-skills-sql Open-source listing, not yet scanned by AgentStack. Follow the source repository for install instructions.
Security review
⚠ Flagged1 finding(s); flagged for manual review. · v0.1.0 How review works →
- • Prompt-injection patterns
- • Secret / credential exfiltration
- • Dangerous shell & filesystem operations
- • Untrusted network calls
- • Known-malicious package signatures
- high Destructive filesystem operation.
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Redpanda SQL (Oxla)
Oxla is a distributed columnar analytical database that speaks the PostgreSQL wire protocol (port 5432). Any psql client, JDBC driver, psycopg2, or pgx application connects to it without modification. Oxla is purpose-built for analytical (OLAP) workloads — large scans, aggregations, multi-table joins, and window functions over wide tables — and differs from standard PostgreSQL in execution architecture (columnar, distributed, parallel) and in which DDL/DML features it supports.
Key differences from PostgreSQL to keep in mind:
- There is no
EXPLAINSQL statement. Query-plan output is controlled by the config flagsfeature_flags.print_query_planandfeature_flags.pipeline_visualization, not by an EXPLAIN command. COPY FROM/TOuses explicitFORMAToption names (CSV,PARQUET,ORC).SELECT INTOsupports two forms:SELECT ... INTO new_table FROM ...(creates a new table) andSELECT ... INTO 'path' (options) FROM ...(writes to a file). The parenthesized option list selects the file form.CREATE TABLE [IF NOT EXISTS] t AS SELECT ...(CTAS) is supported and is the idiomatic way to materialize or reshape a table.GENERATE_SERIES(start, stop[, step])is supported as a table-valued function.
Quickstart
# Connect with psql (default password: oxla)
psql -h localhost -p 5432 -U oxla oxla
-- 1. Create a table
CREATE TABLE orders (
order_id INT,
customer VARCHAR,
region VARCHAR,
amount DOUBLE,
order_date DATE
);
-- 2. Insert a few rows (native VALUES form)
INSERT INTO orders VALUES
(1, 'Acme', 'EMEA', 1250.50, DATE '2024-01-15'),
(2, 'Globex', 'APAC', 3400.00, DATE '2024-01-16'),
(3, 'Acme', 'EMEA', 980.00, DATE '2024-02-01');
-- INSERT … SELECT is the form used for transformed or bulk loads:
INSERT INTO orders SELECT order_id, customer, region, amount * 1.1, order_date
FROM orders_staging;
-- 3. COPY from a Parquet file (local or S3)
-- Local file:
COPY orders FROM 'orders.parquet' (FORMAT PARQUET);
-- 4. Run a GROUP BY aggregation
SELECT region,
COUNT(*) AS order_count,
SUM(amount) AS total_revenue,
AVG(amount) AS avg_order
FROM orders
GROUP BY region
ORDER BY total_revenue DESC;
-- 5. Window function: running total per region
SELECT order_date,
region,
amount,
SUM(amount) OVER (PARTITION BY region ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total
FROM orders
ORDER BY region, order_date;
-- 6. CTE (WITH)
WITH monthly AS (
SELECT EXTRACT(YEAR FROM order_date) AS yr,
EXTRACT(MONTH FROM order_date) AS mo,
SUM(amount) AS revenue
FROM orders
GROUP BY yr, mo
)
SELECT * FROM monthly
ORDER BY yr, mo;
Connecting
See [references/connect-and-types.md](references/connect-and-types.md) for full driver connection strings, SSL config, and the default initial_password.
Default connection parameters (from config/Release/default_config.yml):
| Parameter | Default | |-----------|---------| | Host | localhost | | Port | 5432 | | User | oxla | | Password | oxla | | Database | oxla |
# psql one-liner
PGPASSWORD=oxla psql -h localhost -p 5432 -U oxla oxla
# Python (psycopg2)
conn = psycopg2.connect(host="localhost", port=5432, user="oxla", password="oxla", dbname="oxla")
# Go (pgx)
conn, _ := pgx.Connect(ctx, "postgres://oxla:oxla@localhost:5432/oxla")
Supported Data Types
Full type reference: [references/connect-and-types.md](references/connect-and-types.md).
Types grounded in src/sqlparser/sql/ColumnType.h (the enum class DataType listing):
| Oxla Type | Notes | |-----------|-------| | INT / INTEGER | 32-bit integer (i32) | | LONG / BIGINT | 64-bit integer (i64) | | INT16 | 128-bit (16-byte) wide integer (i128); Oxla-native, not 16-bit | | INT32 | 256-bit (32-byte) wide integer (i256); Oxla-native, not an alias of INT | | FLOAT | 32-bit float | | DOUBLE | 64-bit float | | NUMERIC(p,s) / DECIMAL(p,s) | Fixed-precision | | CHAR(n) / VARCHAR(n) / TEXT | String types | | DATE | Calendar date | | TIME / TIMETZ | Time of day | | TIMESTAMP / TIMESTAMPTZ | Date+time | | INTERVAL | Duration | | BOOL | Boolean | | JSON / JSONB | JSON document | | BYTEA | Binary data | | ARRAY | Arrays (INT[], FLOAT[], etc.) | | GEOMETRY / GEOGRAPHY / POINT | Geospatial |
DDL and DML
Full reference: [references/ddl-dml.md](references/ddl-dml.md).
-- CREATE TABLE
CREATE TABLE sales (
id INT,
product VARCHAR,
qty INT,
price DOUBLE,
sold_at TIMESTAMP
);
-- CREATE TABLE AS SELECT (CTAS — materialize / reshape a table)
CREATE TABLE sales_2024 AS
SELECT * FROM sales WHERE sold_at >= TIMESTAMP '2024-01-01 00:00:00';
-- CREATE TABLE IF NOT EXISTS AS SELECT
CREATE TABLE IF NOT EXISTS sales_backup AS SELECT * FROM sales;
-- DROP TABLE
DROP TABLE sales;
DROP TABLE IF EXISTS sales;
-- NOTE: Oxla does NOT support ALTER TABLE ... ADD/DROP/RENAME COLUMN.
-- The only ALTER TABLE form re-binds an external Redpanda/Kafka catalog table.
-- Use IF EXISTS and the catalog=>table_name external-source form (both required):
-- ALTER TABLE IF EXISTS my_catalog=>my_table WITH (schema_lookup_policy='LATEST')
-- To change a table's schema, use CREATE TABLE AS SELECT to recreate it.
-- NOTE: GRANT ... ON ALL TABLES IN SCHEMA is NOT valid in Oxla (parser rejects
-- the literal 'tables'). Valid targets: ON table, ON TABLE table, ON SCHEMA name,
-- ON DATABASE name, ON EXTERNAL SOURCE catalog. See references/ddl-dml.md.
-- TRUNCATE
TRUNCATE TABLE sales;
-- CREATE / DROP VIEW
CREATE VIEW emea_orders AS
SELECT * FROM orders WHERE region = 'EMEA';
DROP VIEW emea_orders;
-- INSERT — native VALUES form (literal rows)
INSERT INTO sales VALUES (1, 'Widget', 10, 9.99, TIMESTAMP '2024-01-15 12:00:00');
-- INSERT … SELECT (for transformed or bulk loads)
INSERT INTO dest SELECT col1, col2 FROM source WHERE condition;
-- UPDATE / DELETE
UPDATE orders SET amount = 0 WHERE order_id = 1;
DELETE FROM orders WHERE order_date = TIMESTAMP '2024-01-01 00:00:00';
Redpanda/Kafka and Iceberg External Sources (enterprise differentiator)
Full reference: [references/kafka-iceberg.md](references/kafka-iceberg.md).
Oxla can attach Redpanda/Kafka topics and Apache Iceberg tables as external sources and query them with plain SQL — the central Oxla + Redpanda enterprise differentiator. The Redpanda-side features it consumes (Iceberg Topics, Tiered Storage, Cloud Topics) each require a Redpanda Enterprise license.
External tables use the catalog=>table_name reference syntax.
-- 1. Object-storage connection (s3 / gcs / abs)
CREATE STORAGE my_s3 TYPE = S3 WITH (
region = 'us-west-2', access_key_id = 'AKID...', secret_access_key = 'secret...');
-- 2. Iceberg catalog (e.g. Redpanda Iceberg Topics, Glue, Polaris)
CREATE ICEBERG CATALOG my_iceberg STORAGE my_s3 WITH (
uri = 'https://rest-catalog', warehouse = 's3://wh/', auth_type = 'oauth2',
oauth2_client_id = 'id', oauth2_client_secret = 'secret');
-- 3. Redpanda/Kafka catalog (initial_brokers + schema_registry_url required)
CREATE REDPANDA CATALOG my_rp WITH (
initial_brokers = 'localhost:9092', schema_registry_url = 'http://localhost:8081');
-- 4. Bind a topic to a queryable table (topic option required)
CREATE TABLE my_rp=>orders WITH (
topic = 'orders', schema_lookup_policy = 'LATEST', struct_mapping_policy = 'JSON');
-- 5. Query it like any table
SELECT region, COUNT(*) FROM my_rp=>orders GROUP BY region;
-- Rebind/reconfigure (IF EXISTS + catalog=>table both required)
ALTER TABLE IF EXISTS my_rp=>orders WITH (error_handling_policy = 'DROP_RECORD');
-- Refresh external metadata; grant access
REFRESH my_rp=>orders;
GRANT SELECT ON EXTERNAL SOURCE my_rp TO analyst;
Related Redpanda Enterprise Iceberg-topic properties (set on the Redpanda side): redpanda.iceberg.mode (disabled/key_value/value_schema_id_prefix/ value_schema_latest), redpanda.iceberg.delete, redpanda.iceberg.partition.spec, redpanda.iceberg.target.lag.ms, redpanda.iceberg.invalid.record.action (drop or dlq_table; default dlq_table).
Functions and Analytics
Full reference: [references/functions-and-analytics.md](references/functions-and-analytics.md).
Aggregate functions
SELECT COUNT(*), SUM(amount), AVG(amount), MIN(amount), MAX(amount)
FROM orders;
-- Ordered-set aggregates
SELECT percentile_disc(0.5) WITHIN GROUP (ORDER BY amount DESC) AS median FROM orders;
SELECT percentile_cont(ARRAY[0.25, 0.75]) WITHIN GROUP (ORDER BY amount) AS quartiles FROM orders;
SELECT mode() WITHIN GROUP (ORDER BY region) FROM orders;
Window functions
-- Ranking
SELECT order_id, region, amount,
RANK() OVER (PARTITION BY region ORDER BY amount DESC) AS rnk,
DENSE_RANK() OVER (PARTITION BY region ORDER BY amount DESC) AS dense_rnk,
ROW_NUMBER() OVER (PARTITION BY region ORDER BY amount DESC) AS rn
FROM orders;
-- LAG / LEAD
SELECT order_date, amount,
LAG(amount, 1) OVER (ORDER BY order_date) AS prev_amount
FROM orders;
-- Named window
SELECT SUM(amount) OVER w, AVG(amount) OVER w
FROM orders
WINDOW w AS (PARTITION BY region ORDER BY order_date
ROWS BETWEEN 2 PRECEDING AND CURRENT ROW);
String functions
SELECT UPPER(customer), LOWER(region),
CONCAT(customer, ' / ', region),
LENGTH(customer),
SUBSTR(customer, 1, 4),
STARTS_WITH(customer, 'A'),
ENDS_WITH(customer, 'e'),
STRPOS(customer, 'me'),
REPLACE(customer, 'Acme', 'ACME'),
REGEXP_REPLACE(customer, 'G.*', 'Corp', 'i')
FROM orders;
Math functions
SELECT ABS(-1.5), CEIL(1.2), FLOOR(1.9), ROUND(1.567),
SQRT(16.0), EXP(1.0), LN(2.718), LOG10(100.0),
SIN(3.14), COS(0.0), TAN(0.785),
PI(), SIGN(-5);
Date/time functions
SELECT EXTRACT(YEAR FROM order_date),
EXTRACT(MONTH FROM order_date),
EXTRACT(DAY FROM order_date),
TIMESTAMP_TRUNC(sold_at, HOUR),
MAKE_DATE(2024, 3, 15),
CURRENT_TIMESTAMP,
CURRENT_TIMESTAMP(3)
FROM orders;
Joins
-- INNER JOIN
SELECT o.order_id, c.name
FROM orders o
JOIN customers c ON o.customer = c.name;
-- LEFT / RIGHT / FULL JOIN
SELECT o.order_id, p.product_name
FROM orders o
LEFT JOIN products p ON o.product = p.id;
-- FULL OUTER JOIN
SELECT o.order_id, r.label
FROM orders o
FULL JOIN regions r ON o.region = r.code;
-- CROSS JOIN
SELECT * FROM (SELECT amount FROM orders LIMIT 5)
CROSS JOIN (SELECT region FROM regions LIMIT 3);
-- Multi-table (star-schema style)
SELECT d.year, p.category, SUM(f.revenue) AS total
FROM fact_sales f
JOIN dim_date d ON f.date_key = d.date_key
JOIN dim_product p ON f.product_key = p.product_key
GROUP BY d.year, p.category
ORDER BY d.year, total DESC;
Set Operations
UNION, INTERSECT, and EXCEPT each accept an optional ALL qualifier. Without ALL, duplicates are eliminated (distinct semantics).
-- UNION ALL (keeps duplicates)
SELECT region FROM orders WHERE amount > 1000
UNION ALL
SELECT region FROM returns;
-- UNION (distinct)
SELECT region FROM orders
UNION
SELECT region FROM returns;
-- INTERSECT ALL
SELECT region FROM orders
INTERSECT ALL
SELECT region FROM targets;
-- EXCEPT [ALL] — rows in left but not right
SELECT region FROM orders
EXCEPT
SELECT region FROM blacklist;
SELECT i0 FROM tb1
EXCEPT ALL
SELECT i0 FROM tb2;
GENERATE_SERIES
SELECT * FROM generate_series(1, 10);
SELECT * FROM generate_series(0, 100, 10);
SELECT gs FROM generate_series(1, 5) AS gs;
SELECT * FROM generate_series(10, 1, -1);
Reference Directory
- [connect-and-types.md](references/connect-and-types.md): PostgreSQL wire protocol connection (port 5432, psql/JDBC/psycopg2/pgx), authentication (
initial_password), SSL config, and the full supported data-type list grounded inColumnType.h. - [ddl-dml.md](references/ddl-dml.md): CREATE/DROP TABLE, CREATE TABLE AS SELECT (CTAS), CREATE/DROP VIEW, CREATE/DROP SCHEMA, TRUNCATE, CREATE ROLE, GRANT/REVOKE (valid targets: ON table / ON TABLE / ON SCHEMA / ON DATABASE / ON EXTERNAL SOURCE —
ON ALL TABLES IN SCHEMAis rejected by the parser), theALTER TABLE IF EXISTS catalog=>table WITH (...)Kafka-catalog rebind, SELECT/INSERT VALUES/INSERT SELECT/UPDATE/DELETE, SELECT INTO (table or file destination), PREPARE/EXECUTE, and transactions — all grounded inquery_plannertest cases andbison_parser.y. - [kafka-iceberg.md](references/kafka-iceberg.md): Oxla + Redpanda enterprise differentiator — querying Redpanda/Kafka topics and Apache Iceberg tables via SQL. CREATE/ALTER/DROP STORAGE (s3/gcs/abs), CREATE/ALTER/DROP ICEBERG CATALOG (uri/warehouse/authtype oauth2|basic|awssigv4 + nested keys), CREATE/ALTER/DROP REDPANDA|KAFKA CATALOG (initialbrokers/schemaregistryurl required, sasl, truststore, keystore, USING CATALOG bind/detach), CREATE TABLE / ALTER TABLE IF EXISTS catalog=>topic WITH (topic/schemalookuppolicy/errorhandlingpolicy/structmappingpolicy/confluentwireprotocol), REFRESH, DESCRIBE/SHOW, GRANT ON EXTERNAL SOURCE, and the Redpanda Enterprise Iceberg-topic properties (redpanda.iceberg.mode/delete/partition.spec/target.lag.ms/invalid.record.action). Notes Redpanda Enterprise license requirements. Grounded in
bison_parser.y,connection_option_names.h,kafka/conversions.cpp,iceberg_catalog_parser.cpp. - [data-loading.md](references/data-loading.md): COPY FROM / COPY TO with CSV/Parquet/ORC formats, STDIN/STDOUT, S3 credentials via
aws_cred(...), and bulk-loading patterns for analytical ingestion. - [functions-and-analytics.md](references/functions-and-analytics.md): Complete function reference — aggregates (SUM/AVG/COUNT/percentiledisc/percentilecont/mode/CORR), string (CONCAT/SUBSTR/REPLACE/REGEXPREPLACE/STARTSWITH/ENDSWITH/STRPOS/LENGTH/UPPER/LOWER), math (ABS/CEIL/FLOOR/ROUND/SQRT/EXP/LN/LOG10/trig), date-time (EXTRACT/TIMESTAMPTRUNC/MAKEDATE/MAKETIMESTAMP/CURRENT_TIMESTAMP), window functions, CTEs, CASE/IF, and ARRAY functions — all grounded in test cases.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: redpanda-data
- Source: redpanda-data/skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.