mirror of
https://github.com/neondatabase/neon.git
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df6909302b
The new bencmark generates tenants with around 300 SLRU blocks of 8KiB each. Both vectored get implementations are benchmarked. For the vectored implementation validation is disabled.
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
"""
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Utilities used by all code in this sub-directory
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"""
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from typing import Any, Callable, Dict, Tuple
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import fixtures.pageserver.many_tenants as many_tenants
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from fixtures.log_helper import log
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from fixtures.neon_fixtures import (
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NeonEnv,
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NeonEnvBuilder,
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)
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from fixtures.pageserver.utils import wait_until_all_tenants_state
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from fixtures.types import TenantId, TimelineId
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def ensure_pageserver_ready_for_benchmarking(env: NeonEnv, n_tenants: int):
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"""
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Helper function.
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"""
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ps_http = env.pageserver.http_client()
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log.info("wait for all tenants to become active")
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wait_until_all_tenants_state(
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ps_http, "Active", iterations=n_tenants, period=1, http_error_ok=False
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)
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# ensure all layers are resident for predictiable performance
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tenants = [info["id"] for info in ps_http.tenant_list()]
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for tenant in tenants:
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for timeline in ps_http.tenant_status(tenant)["timelines"]:
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info = ps_http.layer_map_info(tenant, timeline)
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for layer in info.historic_layers:
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assert not layer.remote
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log.info("ready")
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def setup_pageserver_with_tenants(
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neon_env_builder: NeonEnvBuilder,
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name: str,
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n_tenants: int,
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setup: Callable[[NeonEnv], Tuple[TenantId, TimelineId, Dict[str, Any]]],
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) -> NeonEnv:
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"""
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Utility function to set up a pageserver with a given number of identical tenants.
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Each tenant is a pgbench tenant, initialize to a certain scale, and treated afterwards
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with a repeat application of (pgbench simple-update workload, checkpoint, compact).
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"""
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def doit(neon_env_builder: NeonEnvBuilder) -> NeonEnv:
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return many_tenants.single_timeline(neon_env_builder, setup, n_tenants)
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env = neon_env_builder.build_and_use_snapshot(name, doit)
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env.start()
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ensure_pageserver_ready_for_benchmarking(env, n_tenants)
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return env
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