list_avg

Applies to VAST Cluster 5.5.

Functions › List, struct and map functions › Aggregate a list. Returns the mean (average) of the elements of a list, one value per row, as a DOUBLE. NULL elements are skipped. It's the same as list_aggregate(l, 'avg').

Syntax

list_avg(l)

Returns: DOUBLE, for lists of integers and decimals too.

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_avg. 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 and measurements. Row 3 has empty lists, row 4 has no values, and row 5 has measurements close to the largest DOUBLE:

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_avg"'

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[])")
run(f"INSERT INTO {TABLE} VALUES "
    "(1, [1, 2, 3], [0.5, 2.5, -1.0]), "
    "(2, [3, NULL, 3], [2.0, NULL]), "
    "(3, [], []), "
    "(4, NULL, NULL), "
    "(5, [-5, 0, -5, 10], [1e308, 1e308, -1e-308])")

[]
[]
[(5,)]

What it does

list_avg(l) adds up the elements of l that aren't NULL and divides by how many there are. list_avg([1, 2]) returns 1.5.

• NULL elements don't count. list_avg([NULL, 4]) returns 4.0: the NULL is skipped, not treated as 0.
• The result is always DOUBLE. list_avg([1.25, 2.50]) returns 1.875, and the mean of two copies of the largest BIGINT returns 9.223372036854776e+18 without overflowing.
• DOUBLE lists follow floating point. list_avg([1e308, 1e308]) returns inf, although the mean, 1e308, fits in a DOUBLE. A NaN element gives nan, and an inf element gives inf.

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_avg(NULL), list_avg(NULL::BIGINT[]), list_avg([]::BIGINT[]), list_avg([NULL, 4]), list_avg([1, 2, 3])")
run("SELECT list_avg([1.25, 2.50]), list_avg([9223372036854775807, 9223372036854775807]), list_avg([1e308, 1e308]), "
    "list_avg(['NaN'::DOUBLE, 1.0]), list_avg(['inf'::DOUBLE, 1.0])")
run("SELECT list_avg([DATE '2026-10-16', DATE '2026-10-18'])")
run(f"SELECT list_avg(123) FROM {TABLE} LIMIT 1")

[(None, None, None, 4.0, 2.0)]
[(1.875, 9.223372036854776e+18, inf, nan, inf)]
InternalError: Binder Error: No matching aggregate function
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 mean of each row's numbers and measurements:

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

[(1, 2.0, 0.6666666666666666), (2, 3.0, 2.0), (3, None, None), (4, None, None), (5, 0.0, inf)]

The rows whose mean isn't NULL, which leaves out the empty and NULL lists:

run(f"SELECT id FROM {TABLE} WHERE list_avg(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_avg() always runs in the Query Engine. It averages 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 mean 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 mean 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_avg(nums) IS NOT NULL")

┌───────────────────────────┐
│         PROJECTION        │
│    ────────────────────   │
│             #1            │
│                           │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│           FILTER          │
│    ────────────────────   │
│ (list_aggr(nums, 'avg') 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_avg() 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 mean 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

• Numbers only. A list of dates, text or intervals fails with No matching aggregate function. A value that isn't a list fails with an error that names list_aggr, the function list_avg is built on.
• For the middle date of a list, use list_median, which takes dates.
• 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_avg(l), list_aggregate(l, 'avg'), list_median(l), list_mode(l), list_sum(l) "
    "FROM (SELECT [3, 1, NULL, 3, 10] AS l)")

[(4.25, 4.25, 3.0, 3, Decimal('17'))]

Clean up:

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

[]
[]

See also

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