Columns and types

VARCHAR

indexCode
indexName
componentCode
componentName
sector
industry

DOUBLE

weight

TIMESTAMP

startDate
endDate

BOOLEAN

isActive

Current logical schema for lake.index_constituents

Inspect the complete lake.index_constituents schema

Filter rows

Scroll sideways to compare all columns. Select a heading to sort.


indexCode	VARCHAR
indexName	VARCHAR
componentCode	VARCHAR
componentName	VARCHAR
sector	VARCHAR
industry	VARCHAR
weight	DOUBLE
startDate	TIMESTAMP
endDate	TIMESTAMP
isActive	BOOLEAN

Current logical schema for lake.index_constituents

Query lake.index_constituents using the column names and types below. The query validator and engine use this logical catalog; a field absent from an older shard returns typed NULL.

What this table contains

Membership of major indices; the only source of GICS-style sector and industry strings.

Inspect S&P 500 membership records

Query example, not executed here. The dates and filters define the proposed research scope; returned rows and current coverage must be checked in your workspace.

componentCode supplies the join ticker; componentName is its display name. Inspect the membership span and status rather than treating this limited preview as a historical point-in-time universe.

SQL

SELECT indexCode, componentCode, componentName, sector, industry,
       startDate, endDate, isActive
FROM lake.index_constituents
WHERE indexCode = 'GSPC'
ORDER BY componentCode, startDate
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the index_constituents example and read back its parts

Run the exact SQL through the Python SDK

Save this page's SQL as query.sql and the runner below as query.py. Use a registered key with lake scope from Developers. The runner submits the displayed index_constituents query once, saves its operation, waits on that same ID, saves the returned manifest and downloads its Parquet parts.

This submission has not been executed for the reference page. Inspect the SQL, dates and resource limits before running it. Reuse the request key only for this identical SQL and limits; use a new key when changing them.

Shell

python3 -m pip install nexustrade==1.42.0
export NEXUSTRADE_API_KEY="YOUR_LAKE_SCOPED_KEY"
python3 query.py

Python

import json
from pathlib import Path
from nexustrade import NexusTradeClient

# Save the SQL displayed on this page as query.sql.
sql = Path("query.sql").read_text()
client = NexusTradeClient.from_environment()
operation = client.create_lake_query(
    {"query": sql, "limits": {"timeoutSeconds": 30, "maxRows": 50,
                              "maxResultBytes": 1048576}},
    idempotency_key="reference-index_constituents-20261012-v1",
)
Path("query-operation.json").write_text(json.dumps(operation, indent=2))
finished = client.wait_for_lake_query(operation["id"])
if finished["status"] != "completed":
    raise RuntimeError(finished)
manifest = client.get_lake_query_manifest(operation["id"])["result"]
Path("query-manifest.json").write_text(json.dumps(manifest, indent=2))
print(json.dumps(manifest, indent=2))
for part in manifest["parts"]:
    Path(f"query-{part['part']}.parquet").write_bytes(
        client.download_lake_query_part(operation["id"], part["part"], max_bytes=1048576)
    )

Create a lake-scoped key ↗Read Parquet results with Python ↗

Coverage and timing

Inspect source coverage and timing before using the rows

Membership of major indices; the only source of GICS-style sector and industry strings.

Filter an index with `indexCode` (`'GSPC'` for the S&P 500). `componentCode` is the ticker to join on and `componentName` is the display name. This is the only table with GICS-style `sector` / `industry` labels — `stock_industries` carries boolean theme tags instead, and the SEC statement tables carry neither.

Discussion

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lake.index_constituents dataset schema