mirror of
https://github.com/neondatabase/neon.git
synced 2026-01-05 20:42:54 +00:00
53f438a8a8
We use the term "endpoint" in for compute Postgres nodes in the web UI
and user-facing documentation now. Adjust the nomenclature in the code.
This changes the name of the "neon_local pg" command to "neon_local
endpoint". Also adjust names of classes, variables etc. in the python
tests accordingly.
This also changes the directory structure so that endpoints are now
stored in:
.neon/endpoints/<endpoint id>
instead of:
.neon/pgdatadirs/tenants/<tenant_id>/<endpoint (node) name>
The tenant ID is no longer part of the path. That means that you
cannot have two endpoints with the same name/ID in two different
tenants anymore. That's consistent with how we treat endpoints in the
real control plane and proxy: the endpoint ID must be globally unique.
31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
import threading
|
|
|
|
import pytest
|
|
from fixtures.compare_fixtures import PgCompare
|
|
from fixtures.neon_fixtures import PgProtocol
|
|
|
|
from performance.test_perf_pgbench import get_scales_matrix
|
|
from performance.test_wal_backpressure import record_read_latency
|
|
|
|
|
|
def start_write_workload(pg: PgProtocol, scale: int = 10):
|
|
with pg.connect().cursor() as cur:
|
|
cur.execute(f"create table big as select generate_series(1,{scale*100_000})")
|
|
|
|
|
|
# Measure latency of reads on one table, while lots of writes are happening on another table.
|
|
# The fine-grained tracking of last-written LSNs helps to keep the latency low. Without it, the reads would
|
|
# often need to wait for the WAL records of the unrelated writes to be processed by the pageserver.
|
|
@pytest.mark.parametrize("scale", get_scales_matrix(1))
|
|
def test_measure_read_latency_heavy_write_workload(neon_with_baseline: PgCompare, scale: int):
|
|
env = neon_with_baseline
|
|
pg = env.pg
|
|
|
|
with pg.connect().cursor() as cur:
|
|
cur.execute(f"create table small as select generate_series(1,{scale*100_000})")
|
|
|
|
write_thread = threading.Thread(target=start_write_workload, args=(pg, scale * 100))
|
|
write_thread.start()
|
|
|
|
record_read_latency(env, lambda: write_thread.is_alive(), "SELECT count(*) from small")
|