import sys;
import threading;
import psycopg2;

def test_multixact(tblname: str):
    with psycopg2.connect() as conn:
        cur = conn.cursor()
        cur.execute(
            f"""
            DROP TABLE IF EXISTS {tblname};
            CREATE TABLE {tblname}(i int primary key, n_updated int) WITH (autovacuum_enabled=false);
            INSERT INTO {tblname} select g, 0 from generate_series(1, 50) g;
            """
        )

    # Lock entries using parallel connections in a round-robin fashion.
    nclients = 50
    update_every = 97
    connections = []
    for _ in range(nclients):
        # Do not turn on autocommit. We want to hold the key-share locks.
        conn = psycopg2.connect()
        connections.append(conn)

    # On each iteration, we commit the previous transaction on a connection,
    # and issue another select. Each SELECT generates a new multixact that
    # includes the new XID, and the XIDs of all the other parallel transactions.
    # This generates enough traffic on both multixact offsets and members SLRUs
    # to cross page boundaries.
    for i in range(20000):
        conn = connections[i % nclients]
        conn.commit()

        # Perform some non-key UPDATEs too, to exercise different multixact
        # member statuses.
        if i % update_every == 0:
            conn.cursor().execute(f"update {tblname} set n_updated = n_updated + 1 where i = {i % 50}")
        else:
            conn.cursor().execute(f"select * from {tblname} for key share")

#nthreads=10
#
#threads = []
#for threadno in range(nthreads):
#    tblname = f"tbl{threadno}"
#    t = threading.Thread(target=test_multixact, args=(tblname,))
#    t.start()
#    threads.append(t)
#
#for threadno in range(nthreads):
#    threads[threadno].join()

test_multixact(sys.argv[1])