mirror of
https://github.com/neondatabase/neon.git
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63 lines
3.2 KiB
SQL
63 lines
3.2 KiB
SQL
CREATE TABLE scrapes (
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scrape_ts timestamp with time zone,
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pageserver_id text,
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pageserver_launch_timestamp timestamp with time zone,
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tenant_id text,
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timeline_id text,
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layer_map_dump jsonb
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);
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create index scrapes_tenant_id_idx on scrapes (tenant_id);
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create index scrapes_timeline_id_idx on scrapes (timeline_id);
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create index scrapes_scrape_ts_idx on scrapes (scrape_ts);
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create index scrapes_tenant_timeline_id_idx on scrapes (tenant_id, timeline_id); -- this can probably go
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create index timeline_scrape_ts_idx on scrapes (timeline_id, scrape_ts);
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create index tenant_timeline_scrape_ts_idx on scrapes (tenant_id, timeline_id, scrape_ts);
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create index pageserver_id_idx on scrapes (pageserver_id);
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-- add statistics so that query planner selects the tenant_timeline_scrape_ts idx
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create statistics scrapes_tenant_timeline_stats on tenant_id, timeline_id from scrapes;
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--- what follows are example queries ---
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--- how many layer accesses did we have per layers/timeline/tenant in the last 30 seconds
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with flattened_to_access_count as (
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select *
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from scrapes as scrapes
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cross join jsonb_to_recordset(scrapes.layer_map_dump -> 'historic_layers') historic_layer(layer_file_name text, access_stats jsonb)
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cross join jsonb_to_record(historic_layer.access_stats) access_stats(access_count_by_access_kind jsonb)
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cross join LATERAL (select key as access_kind, value::numeric as access_count from jsonb_each(access_count_by_access_kind)) access_count
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)
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select tenant_id, timeline_id, layer_file_name, access_kind, SUM(access_count) access_count_sum
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from flattened_to_access_count
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where scrape_ts > (clock_timestamp() - '30 second'::interval)
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group by rollup(tenant_id, timeline_id, layer_file_name, access_kind)
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having SUM(access_count) > 0
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order by access_count_sum desc, tenant_id desc, timeline_id desc, layer_file_name, access_kind;
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--- residence change events in the last 30 minutes
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-- (precise, unless more residence changes happen between scrapes than layer access stats buffer)
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with flattened_to_residence_changes as (select *
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from scrapes as scrapes
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cross join jsonb_to_recordset(scrapes.layer_map_dump -> 'historic_layers') historic_layer(layer_file_name text, access_stats jsonb)
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cross join jsonb_to_record(historic_layer.access_stats) access_stats(residence_events_history jsonb)
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cross join jsonb_to_record(access_stats.residence_events_history) residence_events_history(buffer jsonb, drop_count numeric)
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cross join jsonb_to_recordset(residence_events_history.buffer) residence_events_buffer(status text, reason text, timestamp_millis_since_epoch numeric)
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)
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, renamed as (
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select
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scrape_ts,
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pageserver_launch_timestamp,
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layer_file_name,
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tenant_id,
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timeline_id,
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to_timestamp(timestamp_millis_since_epoch/1000) as residence_change_ts,
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status,
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reason
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from flattened_to_residence_changes
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)
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select distinct residence_change_ts, status, reason, tenant_id, timeline_id, layer_file_name
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from renamed
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where residence_change_ts > (clock_timestamp() - '30 min'::interval)
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order by residence_change_ts desc, layer_file_name;
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