list_median

Applies to VAST Cluster 5.5.

Functions › List, struct and map functions › Aggregate a list. Returns the middle value of the elements of a list, one value per row. NULL elements are skipped. It's the same as list_aggregate(l, 'median'), and it works although the median aggregate over rows isn't available.

Syntax

list_median(l)

Returns: DOUBLE for a list of numbers; the element type for a list of text; TIMESTAMP for a list of dates.

Set up the examples

The examples connect with the ADBC driver, as in VAST Query Engine (ADBC), and work in a schema called docs_sql_fn_lists_list_median. Set QE_ENDPOINT, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and VASTDB_BUCKET (SQL reference), and run the blocks in order in one Python session. Below each block is the output you should see.

This block connects, defines the run() and plan() helpers, and creates a table of five rows of lists of numbers, measurements and words. Row 3 has empty lists and row 4 has no values:

import os
import re

import adbc_driver_manager
import adbc_driver_manager.dbapi as dbapi
import adbc_driver_vastdb

BUCKET = os.environ["VASTDB_BUCKET"]
SCHEMA = f'"{BUCKET}/docs_sql_fn_lists_list_median"'

conn = dbapi.connect(
    driver=adbc_driver_vastdb.get_driver_path(),
    db_kwargs={
        "vast.db.endpoint": os.environ["QE_ENDPOINT"],
        "vast.db.access_key": os.environ["AWS_ACCESS_KEY_ID"],
        "vast.db.secret_key": os.environ["AWS_SECRET_ACCESS_KEY"],
    },
    autocommit=True,
)

def server_message(e):
    """The server's message in a driver error, without the request ID or the echoed statement."""
    text = re.sub(r"\[req=0x[0-9a-f]+\]: ", "", str(e))
    found = (re.search(r'message: "(.*?)", request_id', text, re.S)
             or re.search(r"description: (.*)\)", text, re.S))
    message = found.group(1) if found else text.strip().splitlines()[-1]
    message = re.sub(r"^Plan query: '.*?' failed with: ", "", message, flags=re.S)
    message = re.split(r"\\n|\n", message)[0].replace('\\"', '"')
    return message.replace(BUCKET, "<bucket>")

def run(sql):
    """Run one statement. Print the rows it returns, or the error."""
    try:
        with conn.cursor() as cur:
            cur.execute(sql)
            rows = cur.fetchall() if cur.description else []
    except (adbc_driver_manager.Error, OSError) as e:
        print(f"{type(e).__name__}: {server_message(e)}")
    else:
        print(rows)

def plan(sql):
    """Print the plan without its ~N Rows lines: the table's row count, which can still be 0 just after an insert."""
    with conn.cursor() as cur:
        cur.execute("EXPLAIN " + sql)
        steps = cur.fetchall()[0][1]
    print("\n".join(line for line in steps.splitlines() if "Rows" not in line))

TABLE = f"{SCHEMA}.samples"
run(f"CREATE SCHEMA IF NOT EXISTS {SCHEMA}")
run(f"CREATE TABLE IF NOT EXISTS {TABLE} (id BIGINT, nums BIGINT[], vals DOUBLE[], words VARCHAR[])")
run(f"INSERT INTO {TABLE} VALUES "
    "(1, [1, 2, 3], [0.5, 2.5, -1.0], ['a', 'b', 'c']), "
    "(2, [3, NULL, 3], [2.0, NULL], ['b', NULL, 'B']), "
    "(3, [], [], []), "
    "(4, NULL, NULL, NULL), "
    "(5, [-5, 0, -5, 10], [1e308, 1e308, -1e-308], ['\u00e9', 'Zo\u00eb', '\U0001F1EB\U0001F1F7', ''])")

[]
[]
[(5,)]

What it does

list_median(l) sorts the elements of l that aren't NULL and returns the one in the middle. list_median([1, 3, 2]) returns 2.0.

• An even count of numbers gives the mean of the two middle values. list_median([1, 2, 3, 4]) returns 2.5.
• An even count of text values gives the lower of the two. Text can't be averaged, so list_median(['a', 'b']) returns 'a', and list_median(['a', 'b', 'c', 'd']) returns 'b'. In the example table, row 2's words 'b' and 'B' have the median 'B'.
• Dates give a TIMESTAMP. The median of DATE '2026-10-16' and DATE '2026-10-18' is 2026-10-17 00:00:00.
• The result for numbers is DOUBLE, for integer lists too.

NULLs and edge cases

The same cases, as the cluster returns them. They use the table and helpers from Set up the examples:

run("SELECT list_median(NULL), list_median(NULL::BIGINT[]), list_median([]::BIGINT[]), "
    "list_median([NULL, 1, NULL]), list_median([1, 2]), list_median(['a', 'b'])")
run("SELECT list_median([DATE '2026-10-16', DATE '2026-10-18']), "
    "list_median([9223372036854775807, 9223372036854775806]), list_median(['NaN'::DOUBLE, 1.0, 2.0])")
run(f"SELECT list_median(123) FROM {TABLE} LIMIT 1")

[(None, None, None, 1.0, 1.5, 'a')]
[(datetime.datetime(2026, 10, 17, 0, 0), 9.223372036854776e+18, 1.0)]
InternalError: Binder Error: No function matches the given name and argument types 'list_aggr(INTEGER_LITERAL, STRING_LITERAL)'. You might need to add explicit type casts.

Examples

The middle value of each row's numbers, measurements and words:

run(f"SELECT id, list_median(nums), list_median(vals), list_median(words) FROM {TABLE} ORDER BY id LIMIT 10")

[(1, 2.0, 0.5, 'b'), (2, 3.0, 2.0, 'B'), (3, None, None, None), (4, None, None, None), (5, -2.5, 1e+308, 'Zoë')]

Odd and even counts, for numbers and text:

run("SELECT list_median([1, 3, 2]), list_median([1, 2, 3, 4]), list_median(['a', 'b', 'c', 'd'])")

[(2.0, 2.5, 'b')]

The rows that have a median:

run(f"SELECT id FROM {TABLE} WHERE list_median(nums) IS NOT NULL ORDER BY id LIMIT 10")

[(1,), (2,), (5,)]

How it runs in VAST

The Query Engine runs on the cluster's CNodes, and a query on a table starts with a scan in VAST DataBase (VAST_SCAN). The scan reads a list column whole, and list_median() always runs in the Query Engine. It works within each row's list, so it's a scalar function: it returns one value per row and needs no GROUP BY.

• In the SELECT list, a projection step computes the median of each row's list after the scan. The plan has no FILTER step.
• In WHERE, the scan sends the list for every row, and a FILTER step computes the median and keeps the rows that match. No rows are skipped in storage.

The plan of the WHERE example shows the FILTER step above the scan:

plan(f"SELECT id FROM {TABLE} WHERE list_median(nums) IS NOT NULL")

┌───────────────────────────┐
│         PROJECTION        │
│    ────────────────────   │
│             #1            │
│                           │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│           FILTER          │
│    ────────────────────   │
│ (list_aggr(nums, 'median')│
│        IS NOT NULL)       │
│                           │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│         VAST_SCAN         │
│    ────────────────────   │
│    Function: VAST_SCAN    │
│                           │
│        Projections:       │
│            nums           │
│             id            │
│                           │
└───────────────────────────┘

Make it faster

• Add a condition on a plain column. A comparison of a column with a constant, such as id > 1, is evaluated in VAST DataBase, so list_median() runs only on the rows that pass it. The plan for id > 1 alone has no FILTER step:

plan(f"SELECT id FROM {TABLE} WHERE id > 1")

┌───────────────────────────┐
│         VAST_SCAN         │
│    ────────────────────   │
│    Function: VAST_SCAN    │
│      Projections: id      │
│                           │
└───────────────────────────┘

• Store the median if you filter on it often. Compute it when you write the rows and keep it in its own column, where a comparison with a constant is pushed down.

Limitations and errors

• Lists only. A value that isn't a list fails with an error that names list_aggr, the function list_median is built on.
• No median over rows. The median aggregate over a table's rows isn't available. list_median works on one row's list.
• ORDER BY needs a LIMIT. Without one, the query fails with The operator 'ORDER_BY' type=1 is currently not supported for this usage.

Similar functions

run("SELECT list_median(l), list_aggregate(l, 'median'), list_aggregate(l, 'quantile_cont', 0.5), list_avg(l), list_mode(l) "
    "FROM (SELECT [3, 1, NULL, 3, 10] AS l)")

[(3.0, 3.0, 3.0, 4.25, 3)]

Clean up:

run(f"DROP TABLE IF EXISTS {TABLE}")
run(f"DROP SCHEMA IF EXISTS {SCHEMA}")
conn.close()

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See also

• List, struct and map functions: all the list aggregates.
• Aggregate functions: aggregates over the rows of a table.
• Where filters run and pushdowns.
• EXPLAIN: how to read a plan.