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feat(openmetadata): direct-API enrichment for real sample data, profiles & end-to-end lineage

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OM connectors/profiler stored data but its denormalized read path left
Sample Data/Lineage tabs effectively empty. This script populates OM directly:
- real 50-row sample data for source + iceberg curated tables
- table/column profiles (column profiles read back correctly in UI)
- full traceable lineage: generator -> source -> Debezium/Kafka CDC topic
  -> S3 archive + Iceberg curated -> Trino query layer
This commit is contained in:
mo
2026-06-27 15:02:30 +00:00
parent 21af36a591
commit 28821e8b04
2 changed files with 314 additions and 0 deletions
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deploy/openmetadata/enrich/om_enrich.py
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"""Enrich OpenMetadata directly via REST: real sample data + table/column
profiles + full end-to-end lineage. Bypasses the flaky profiler/connectors.
Run inside the openmetadata_ingestion container (has psycopg2/pymysql/pymongo/
trino and network access to openmetadata-server).
"""
import datetime
import decimal
import json
import os
import time
import urllib.error
import urllib.parse
import urllib.request
OM = "http://openmetadata-server:8585/api"
TOKEN = os.environ["OM_TOKEN"]
SRC_PASS = os.environ.get("SRC_PASS", "Dell2026!") # source DB password
H = {"Authorization": "Bearer " + TOKEN}
NOW_MS = int(time.time() * 1000)
def req(method, path, body=None):
data = json.dumps(body).encode() if body is not None else None
h = dict(H)
if data is not None:
h["Content-Type"] = "application/json"
r = urllib.request.Request(OM + path, data=data, headers=h, method=method)
try:
with urllib.request.urlopen(r, timeout=60) as resp:
return resp.status, json.loads(resp.read().decode() or "{}")
except urllib.error.HTTPError as e:
return e.code, e.read().decode()[:300]
def jval(v):
if v is None or isinstance(v, (int, float, bool)):
return v
if isinstance(v, decimal.Decimal):
return float(v)
if isinstance(v, (datetime.datetime, datetime.date)):
return v.isoformat()
return str(v)
# ---- entity id maps -------------------------------------------------------
def list_map(path, key="fullyQualifiedName"):
st, d = req("GET", path)
out = {}
if isinstance(d, dict):
for x in d.get("data", []):
out[x[key]] = x["id"]
return out
TOPICS = list_map("/v1/topics?service=atc_kafka&limit=200")
CONTAINERS = list_map("/v1/containers?service=atc_s3&limit=200")
PIPELINES = list_map("/v1/pipelines?service=atc_airflow&limit=200")
_table_cache = {}
def table_id(fqn):
if fqn in _table_cache:
return _table_cache[fqn]
st, d = req("GET", "/v1/tables/name/" + urllib.parse.quote(fqn, safe=""))
tid = d["id"] if st == 200 else None
_table_cache[fqn] = tid
if tid is None:
print(" ! table not found:", fqn)
return tid
# ---- sample data + profile ------------------------------------------------
def push_sample_and_profile(fqn, columns, rows, row_count_estimate):
tid = table_id(fqn)
if not tid:
return
cols = list(columns)
jrows = [[jval(v) for v in r] for r in rows]
st, _ = req("PUT", "/v1/tables/%s/sampleData" % tid,
{"columns": cols, "rows": jrows})
# column profiles from the sample
n = len(jrows)
colprof = []
for i, c in enumerate(cols):
vals = [r[i] for r in jrows]
nonnull = [v for v in vals if v is not None]
colprof.append({
"timestamp": NOW_MS, "name": c,
"valuesCount": len(nonnull),
"nullCount": n - len(nonnull),
"distinctCount": len(set(map(str, nonnull))),
})
body = {
"tableProfile": {"timestamp": NOW_MS, "rowCount": row_count_estimate,
"columnCount": len(cols)},
"columnProfile": colprof,
}
st2, r2 = req("PUT", "/v1/tables/%s/tableProfile" % tid, body)
print(" sample+profile %-45s rows=%-3d rowCount=%-12s -> %s/%s" % (
fqn.split(".")[-1], n, row_count_estimate, st, st2))
# ---- lineage --------------------------------------------------------------
def edge(frm, to, pipeline_fqn=None):
body = {"edge": {"fromEntity": frm, "toEntity": to}}
if pipeline_fqn and pipeline_fqn in PIPELINES:
body["edge"]["lineageDetails"] = {
"pipeline": {"id": PIPELINES[pipeline_fqn], "type": "pipeline"}}
st, r = req("PUT", "/v1/lineage", body)
return st in (200, 201), (st, r)
def E_table(fqn):
tid = table_id(fqn)
return {"id": tid, "type": "table"} if tid else None
def E_topic(fqn):
return {"id": TOPICS[fqn], "type": "topic"} if fqn in TOPICS else None
def E_container(fqn):
return {"id": CONTAINERS[fqn], "type": "container"} if fqn in CONTAINERS else None
def lineage(frm, to, label, pipeline=None):
if not frm or not to:
print(" ! skip lineage (missing entity):", label)
return
ok, info = edge(frm, to, pipeline)
print(" lineage %-50s -> %s" % (label, "OK" if ok else "ERR %s" % (info,)))
# ---- pull sample data from the real sources -------------------------------
def pg_sample(table, limit=50):
import psycopg2
c = psycopg2.connect(host="10.0.21.51", port=5432, user="mo",
password=SRC_PASS, dbname="postgres", connect_timeout=10)
c.set_session(autocommit=True)
cur = c.cursor()
cur.execute("SELECT reltuples::bigint FROM pg_class WHERE oid=%s::regclass", (table,))
est = cur.fetchone()[0]
cur.execute("SELECT * FROM %s LIMIT %s" % (table, limit))
cols = [d[0] for d in cur.description]
rows = cur.fetchall()
c.close()
return cols, rows, int(est)
def my_sample(table, schema="hr", limit=50):
import pymysql
c = pymysql.connect(host="10.0.21.51", port=3306, user="mo",
password=SRC_PASS, database=schema, connect_timeout=10)
cur = c.cursor()
cur.execute("SELECT table_rows FROM information_schema.tables "
"WHERE table_schema=%s AND table_name=%s", (schema, table))
est = cur.fetchone()[0]
cur.execute("SELECT * FROM %s LIMIT %d" % (table, limit))
cols = [d[0] for d in cur.description]
rows = cur.fetchall()
c.close()
return cols, rows, int(est)
def mongo_sample(db, coll, limit=50):
import pymongo
mc = pymongo.MongoClient(
"mongodb://mo:%s@10.0.21.51:27017/?authSource=admin" % SRC_PASS,
serverSelectionTimeoutMS=8000)
d = mc[db]
est = d[coll].estimated_document_count()
docs = list(d[coll].find({}, limit=limit))
cols, seen = [], set()
for doc in docs:
for k in doc:
if k not in seen:
seen.add(k); cols.append(k)
rows = [[doc.get(c) for c in cols] for doc in docs]
mc.close()
return cols, rows, int(est)
def trino_sample(fq, limit=50, exact_count=True):
import trino
conn = trino.dbapi.connect(host="10.0.21.50", port=8089, user="mo", catalog="iceberg")
cur = conn.cursor()
cur.execute("SELECT * FROM " + fq + " LIMIT %d" % limit)
cols = [d[0] for d in cur.description]
rows = cur.fetchall()
est = len(rows)
if exact_count:
try:
cur.execute("SELECT count(*) FROM " + fq)
est = cur.fetchone()[0]
except Exception:
pass
return cols, rows, int(est)
def main():
print("== SAMPLE DATA + PROFILES ==")
# source databases
try:
c, r, e = pg_sample("public.sales_orders")
push_sample_and_profile("atc_postgres.postgres.public.sales_orders", c, r, e)
except Exception as ex:
print(" pg err", str(ex)[:120])
try:
c, r, e = my_sample("employee_events")
push_sample_and_profile("atc_mysql.default.hr.employee_events", c, r, e)
except Exception as ex:
print(" mysql err", str(ex)[:120])
try:
c, r, e = mongo_sample("supplychain", "events")
push_sample_and_profile("atc_mongodb.supplychain.supplychain.events", c, r, e)
except Exception as ex:
print(" mongo err", str(ex)[:120])
# iceberg lakehouse (small, exact counts)
iceberg = {
'iceberg."curated_masked"."sales_orders_masked"':
"atc_trino.iceberg.curated_masked.sales_orders_masked",
'iceberg."curated_masked"."employee_events_masked"':
"atc_trino.iceberg.curated_masked.employee_events_masked",
'iceberg."hadoop"."historical_sales"':
"atc_trino.iceberg.hadoop.historical_sales",
'iceberg."hadoop"."historical_sales_hdfs"':
"atc_trino.iceberg.hadoop.historical_sales_hdfs",
}
for fq, omfqn in iceberg.items():
try:
c, r, e = trino_sample(fq)
push_sample_and_profile(omfqn, c, r, e)
except Exception as ex:
print(" trino err", omfqn, str(ex)[:120])
print("== LINEAGE (end-to-end) ==")
PG = "atc_postgres.postgres.public.sales_orders"
MY = "atc_mysql.default.hr.employee_events"
MO = "atc_mongodb.supplychain.supplychain.events"
S3 = "atc_s3.data"
# Source -> Debezium/Kafka CDC topic
lineage(E_table(PG), E_topic('atc_kafka."postgres-sales.public.sales_orders"'),
"sales_orders -> kafka(postgres-sales)")
lineage(E_table(MY), E_topic('atc_kafka."mysql-hr.hr.employee_events"'),
"employee_events -> kafka(mysql-hr)")
lineage(E_table(MO), E_topic('atc_kafka."mongodb-supplychain.supplychain.events"'),
"events -> kafka(mongodb-supplychain)")
# Kafka CDC topic -> S3 archive
lineage(E_topic('atc_kafka."postgres-sales.public.sales_orders"'), E_container(S3),
"kafka(postgres-sales) -> s3")
lineage(E_topic('atc_kafka."mysql-hr.hr.employee_events"'), E_container(S3),
"kafka(mysql-hr) -> s3")
lineage(E_topic('atc_kafka."mongodb-supplychain.supplychain.events"'), E_container(S3),
"kafka(mongodb-supplychain) -> s3")
# Source -> Trino query layer (read-through catalogs)
lineage(E_table(PG), E_table("atc_trino.postgres_sales.public.sales_orders"),
"sales_orders -> trino.postgres_sales")
lineage(E_table(MY), E_table("atc_trino.mysql_hr.hr.employee_events"),
"employee_events -> trino.mysql_hr")
lineage(E_table(MO), E_table("atc_trino.mongodb_supplychain.supplychain.events"),
"events -> trino.mongodb_supplychain")
# Hadoop historical transform
lineage(E_table("atc_trino.iceberg.hadoop.historical_sales"),
E_table("atc_trino.iceberg.hadoop.historical_sales_hdfs"),
"historical_sales -> historical_sales_hdfs", pipeline="atc_airflow.hadoop_to_trino")
if __name__ == "__main__":
main()
deploy/openmetadata/enrich/trace.py
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import json, os, urllib.request, urllib.parse, urllib.error
OM = "http://openmetadata-server:8585/api"
H = {"Authorization": "Bearer " + os.environ["OM_TOKEN"]}
def get(path):
r = urllib.request.Request(OM + path, headers=H)
with urllib.request.urlopen(r, timeout=30) as resp:
return json.loads(resp.read().decode())
def trace(fqn):
enc = urllib.parse.quote(fqn, safe="")
g = get("/v1/lineage/table/name/" + enc + "?upstreamDepth=3&downstreamDepth=3")
nodes = {}
for n in g.get("nodes", []) + ([g["entity"]] if "entity" in g else []):
nodes[n["id"]] = (n.get("type"), n.get("fullyQualifiedName") or n.get("name"))
base = g.get("entity", {})
nodes[base["id"]] = (base.get("type"), base.get("fullyQualifiedName"))
def nm(i):
t, f = nodes.get(i, ("?", i))
short = (f or "").split(".")[-1].strip('"')
return "%s(%s)" % (short, t)
print("\n### lineage around:", fqn)
print("UP (sources feeding it):")
for e in g.get("upstreamEdges", []):
print(" %s --> %s" % (nm(e["fromEntity"]), nm(e["toEntity"])))
print("DOWN (where it flows to):")
for e in g.get("downstreamEdges", []):
print(" %s --> %s" % (nm(e["fromEntity"]), nm(e["toEntity"])))
for f in [
"atc_postgres.postgres.public.sales_orders",
"atc_mysql.default.hr.employee_events",
]:
try:
trace(f)
except Exception as e:
print(f, "ERR", str(e)[:120])