Hi, thanks for reaching out.
Direct spatial queries like the one you tried on a huge table such as nsc_dr2.meas will likely time out... There are 68 billion rows to go through, despite indexing on the DB backend etc.
But you can get the desired result by doing your spatial query on the much smaller object table, and then JOIN on the meas table using the object IDs. I've rewritten your query accordingly (this example I ran in a Jupyter notebook):
query = """
SELECT o.ra, o.dec, m.*
FROM nsc_dr2.object AS o
JOIN nsc_dr2.meas AS m ON o.id = m.objectid
WHERE (o.ra BETWEEN 4.3683562 and 4.3689118) AND (o.dec BETWEEN -73.4288328 and -73.4282772)
result = qc.query(sql=query,fmt='pandas')
This returns 25 rows (34 colums) in 200 ms:
CPU times: user 32.7 ms, sys: 2.94 ms, total: 35.6 ms
Wall time: 201 ms