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The compute_ctl HTTP server has the following purposes: - Allow management via the control plane - Provide an endpoint for scaping metrics - Provide APIs for compute internal clients - Neon Postgres extension for installing remote extensions - local_proxy for installing extensions and adding grants The first two purposes require the HTTP server to be available outside the compute. The Neon threat model is a bad actor within our internal network. We need to reduce the surface area of attack. By exposing unnecessary unauthenticated HTTP endpoints to the internal network, we increase the surface area of attack. For endpoints described in the third bullet point, we can just run an extra HTTP server, which is only bound to the loopback interface since all consumers of those endpoints are within the compute.
85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
from __future__ import annotations
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import requests
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from fixtures.benchmark_fixture import MetricReport, NeonBenchmarker
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from fixtures.neon_fixtures import NeonEnvBuilder
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# Just start and measure duration.
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#
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# This test runs pretty quickly and can be informative when used in combination
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# with emulated network delay. Some useful delay commands:
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#
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# 1. Add 2msec delay to all localhost traffic
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# `sudo tc qdisc add dev lo root handle 1:0 netem delay 2msec`
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#
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# 2. Test that it works (you should see 4ms ping)
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# `ping localhost`
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#
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# 3. Revert back to normal
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# `sudo tc qdisc del dev lo root netem`
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#
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# NOTE this test might not represent the real startup time because the basebackup
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# for a large database might be larger if there's a lof of transaction metadata,
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# or safekeepers might need more syncing, or there might be more operations to
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# apply during config step, like more users, databases, or extensions. By default
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# we load extensions 'neon,pg_stat_statements,timescaledb,pg_cron', but in this
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# test we only load neon.
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def test_startup_simple(neon_env_builder: NeonEnvBuilder, zenbenchmark: NeonBenchmarker):
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neon_env_builder.num_safekeepers = 3
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env = neon_env_builder.init_start()
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env.create_branch("test_startup")
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endpoint = None
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# We do two iterations so we can see if the second startup is faster. It should
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# be because the compute node should already be configured with roles, databases,
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# extensions, etc from the first run.
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for i in range(2):
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# Start
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with zenbenchmark.record_duration(f"{i}_start_and_select"):
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if endpoint:
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endpoint.start()
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else:
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endpoint = env.endpoints.create_start(
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"test_startup",
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# Shared buffers need to be allocated during startup, so they
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# impact startup time. This is the default value we use for
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# 1CPU pods (maybe different for VMs).
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#
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# TODO extensions also contribute to shared memory allocation,
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# and this test doesn't include all default extensions we
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# load.
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config_lines=["shared_buffers=262144"],
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)
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endpoint.safe_psql("select 1;")
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# Get metrics
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metrics = requests.get(
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f"http://localhost:{endpoint.external_http_port}/metrics.json"
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).json()
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durations = {
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"wait_for_spec_ms": f"{i}_wait_for_spec",
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"sync_safekeepers_ms": f"{i}_sync_safekeepers",
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"sync_sk_check_ms": f"{i}_sync_sk_check",
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"basebackup_ms": f"{i}_basebackup",
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"start_postgres_ms": f"{i}_start_postgres",
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"config_ms": f"{i}_config",
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"total_startup_ms": f"{i}_total_startup",
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}
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for key, name in durations.items():
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value = metrics[key]
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zenbenchmark.record(name, value, "ms", report=MetricReport.LOWER_IS_BETTER)
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# Check basebackup size makes sense
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basebackup_bytes = metrics["basebackup_bytes"]
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if i > 0:
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assert basebackup_bytes < 100 * 1024
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# Stop so we can restart
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endpoint.stop()
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# Imitate optimizations that console would do for the second start
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endpoint.respec(skip_pg_catalog_updates=True)
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