From ff925d2ab37257628cc09b3815186bb35a364a43 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Wed, 14 Oct 2026 13:52:35 -0400 Subject: [PATCH] eval: e2e 10/s plans with one-scrape instant RQEs and one RQE per query Regenerates rqe-optimizer/results/e2e-rate10 (classic, all, multigroup at shared = 1, 10 samples/s) on main: the denser grid's cost table and curves (#213), instant RQEs reading one scrape (#215), and one RQE per schema query (#216). Tables, --runs 3 results and --runs 1 plans; the README gives the RQE counts, costs, classic deployments and ASAPQuery's --data-ingestion-interval-ms 100. Co-Authored-By: Claude Opus 5.5 --- rqe-optimizer/results/e2e-rate10/README.md | 115 +- .../synthetic-templatesall-shared1.json | 2 +- .../synthetic-templatesclassic-shared1.json | 2 +- ...synthetic-templatesmultigroup-shared1.json | 2 +- ...nthetic-templatesall-shared1.plans.json.gz | Bin 60858 -> 39806 bytes ...tic-templatesclassic-shared1.plans.json.gz | Bin 21637 -> 19845 bytes ...-templatesmultigroup-shared1.plans.json.gz | Bin 24445 -> 20632 bytes .../synthetic-templatesall-shared1-tp95.json | 2987 ++++++++--------- ...nthetic-templatesclassic-shared1-tp95.json | 2762 ++++++++------- ...etic-templatesmultigroup-shared1-tp95.json | 2754 ++++++++------- 10 files changed, 4285 insertions(+), 4339 deletions(-) diff --git a/rqe-optimizer/results/e2e-rate10/README.md b/rqe-optimizer/results/e2e-rate10/README.md index 8b8eda4d..12ef825a 100644 --- a/rqe-optimizer/results/e2e-rate10/README.md +++ b/rqe-optimizer/results/e2e-rate10/README.md @@ -4,13 +4,21 @@ The classic, multi-grouping and all (mixed + multi-grouping) workloads of `../autosketch-vs-asap-hydra/` (r = 1, m = 1), re-planned at 1/20 of the data rate: each series is scraped 10 times a second (every 100 ms) instead of 200, so the classic metric carries 1e5 samples/s, `http` 1e5 records/s and `flows` -2e5 records/s. Everything else (templates, windows, accuracy curves, cost -table, p95 targets, runner) is the same as `../autosketch-vs-asap-hydra/`. +2e5 records/s. The templates, windows, accuracy curves, cost table, p95 +targets and runner are those of `../autosketch-vs-asap-hydra/`, with two +differences for the end-to-end run: + +- **Instant RQEs read one scrape.** `sum by (job)` and `quantile by (job)` + look back 100 ms, one sample per series (N = 1e3 per job), and are served + by 100 ms tumbling windows. +- **One RQE per schema query.** A query that several templates ask (14, 15, + 17) is only its strictest template's RQE, so every e2e query is answered by + one deployment. §6.3 keeps every copy (`--keep-duplicate-queries`). ## Files - `inputs/tables/`: the tables, `export_autosketch_eval_table.py --synthetic - --samples-per-sec 10 --keep-duplicate-queries` (classic, all, multigroup at shared = 1). Each one's + --samples-per-sec 10` (classic, all, multigroup at shared = 1). Each one's `workloads[0].e2e` holds the queries and metrics to generate. - `plans/.plans.json.gz`: each method's chosen plans, in the format of `../autosketch-vs-asap-hydra/README.md`, "Plans" (`--runs 1`, kept, then @@ -32,70 +40,84 @@ the RQEs and the e2e queries, and logs it. The same groupings at 15m (1,210 items) are kept. With the two RQEs left in, the runner drops exactly these two as unservable (ASAP and AutoSketch alike) and picks the same plans. -| Workload | RQEs at 200/s | RQEs at 10/s | Served | Dropped | +| Workload | RQEs at 200/s (§6.3) | Duplicate queries | Below the curves | RQEs at 10/s, all served | |---|---|---|---|---| -| classic | 42 | 42 | 42 | 0 | -| multigroup | 62 | 60 | 60 | 0 | -| all | 104 | 102 | 102 | 0 | +| classic | 42 | 0 | 0 | 42 | +| multigroup | 62 | 10 | 2 | 50 | +| all | 104 | 10 | 2 | 92 | Every chosen accuracy is measured (no theory reads), and no Hydra grid is chosen. ## Predicted cost -Cheapest plan (no latency bound), 200/s (`../autosketch-vs-asap-hydra/`) → 10/s: +Cheapest plan (no latency bound) at 10/s: | Workload | Weights | ASAP | ASAP (no roll-ups) | PerQuery | AutoSketch | |---|---|---|---|---|---| -| classic | CPU (vCPU) | 2.38 → 0.309 | 2.38 → 0.309 | 7.77 → 0.725 | 2,739 → 137 | -| classic | Fargate ($/h) | 0.170 → 0.0787 | 0.170 → 0.0787 | 0.623 → 0.324 | 136 → 7.02 | -| multigroup | CPU | 0.221 → 0.0950 | 0.551 → 0.114 | 1.07 → 0.140 | 10.2 → 0.554 | -| multigroup | Fargate | 0.0137 → 0.00872 | 0.0339 → 0.0158 | 0.0597 → 0.0214 | 0.430 → 0.0382 | -| all | CPU | 2.60 → 0.404 | 2.93 → 0.424 | 8.84 → 0.865 | 2,750 → 138 | -| all | Fargate | 0.184 → 0.0874 | 0.204 → 0.0945 | 0.683 → 0.345 | 136 → 7.06 | +| classic | CPU (vCPU) | 0.318 | 0.318 | 0.699 | 135 | +| classic | Fargate ($/h) | 0.0585 | 0.0585 | 0.192 | 6.41 | +| multigroup | CPU | 0.0911 | 0.110 | 0.132 | 0.487 | +| multigroup | Fargate | 0.00846 | 0.0155 | 0.0204 | 0.0348 | +| all | CPU | 0.409 | 0.428 | 0.831 | 136 | +| all | Fargate | 0.0670 | 0.0740 | 0.212 | 6.45 | ASAP, CPU weights: | Workload | Memory (GiB) | Deployments | RQEs rolled up | |---|---|---|---| -| classic | 21.4 → 15.3 | 6 → 6 | 0 → 0 | -| multigroup | 1.06 → 1.34 | 5 → 9 | 52 → 41 | -| all | 22.4 → 16.7 | 11 → 15 | 52 → 41 | - -Only ingest CPU scales with the rate. In the cost model, insert, merge and -query CPU and per-instance memory are constants of the cost table's row, -whatever a window holds; only DDSketch's memory is capped by the values it -holds. AutoSketch's cost is almost all ingest and falls 20×. ASAP's ingest -falls ~10× (classic: 1.84 → 0.18 vCPU), because with cheaper ingest the MILP -picks longer windows with fewer merges: - -| Classic deployment | Window/slide at 200/s → 10/s (s) | +| classic | 10.7 | 7 | 0 | +| multigroup | 1.33 | 9 | 33 | +| all | 12.0 | 16 | 33 | + +Classic deployments (ASAP, CPU weights): + +| Deployment | Window/slide (s) | |---|---| -| `quantile_over_time` (DDSketch α = 0.05) | 300/60 → 900/60 | -| `rate` (exact-increase) | 60/60 → 180/60 | -| top-k (CMS 3×1024) | 60/60 → 300/60 (heap 46,080 → 9,216) | - -ASAP's merge and query CPU barely moves (classic 0.54 → 0.13 vCPU, -multigroup 0.106 → 0.078), so it is half or more of the cost at 10/s. Memory -barely moves either, so Fargate costs fall only ~2×. On multigroup, separate -HLL deployments at coarse groupings now cost less than rolling up from fine -ones. The p99 cube moves from DDSketch α = 0.02 to α = 0.05 and still serves -11 RQEs by roll-up. Roll-ups save 17% on multigroup (60% at 200/s) and 5% on -all (11%). With the multi-grouping templates the tightest feasible latency -bound stays 490 ms (SLAs from 1,000 ms); classic's is 21 ms. +| `sum by (job)` (exact-sum) | 0.1/0.1 | +| `quantile by (job)` (DDSketch α = 0.1) | 0.1/0.1 | +| `sum by (job) (sum_over_time)` (exact-sum) | 60/60 | +| `sum_over_time` (exact-sum) | 60/60 | +| `quantile_over_time` (DDSketch α = 0.1) | 900/60 | +| `rate` (exact-increase) | 180/60 | +| top-k (CMS 3×1024, heap 9,216) | 300/60 | + +Against the previous 10/s plans (1 s instant lookback, every duplicate query, +the cost table and curves before the denser grid): + +- **Memory falls ~30%** (classic 15.3 → 10.7 GiB), and Fargate costs ~25% + (classic 0.0787 → 0.0585 $/h): with the denser grid, plans pick DDSketch + α = 0.1, the coarsest that meets the target. +- **CPU rises ~3%** (classic 0.309 → 0.318 vCPU, +1 deployment): + `sum by (job) (sum_over_time)` no longer shares the instant `sum by (job)` + deployment, whose window is now 100 ms. +- **Multigroup serves 10 fewer RQEs** and costs ~4% less CPU; 33 RQEs are + rolled up (was 41). +- The tightest feasible latency bounds are unchanged: classic 21 ms, multigroup + and all 490 ms. + +Every e2e query's RQEs are served by one deployment in every plan. An +independent re-plan in another session gave identical tables and identical +plans in all 120 (method, weights, kind, SLA) combinations. + +## Converting the plans in ASAPQuery + +`--planner saved` needs `--data-ingestion-interval-ms 100` on these tables: +the scrape interval, which instant RQEs' 0.1 s windows are whole multiples +of. With it, on ASAPQuery `main` (ProjectASAP/ASAPQuery#866), every ASAP plan +converts: classic PromQL 12/12, multigroup SQL 8/8, all SQL 8/8 and PromQL +8/8. ## Reproduce ```sh cargo build --release -p rqe-optimizer --examples DIR=rqe-optimizer/results/e2e-rate10; TMP=$(mktemp -d) -python3 scripts/export_autosketch_eval_table.py --synthetic --samples-per-sec 10 --keep-duplicate-queries \ - --revision 44ac160dd350f695a09224fd124f0dcfe3dd825a --out $TMP/classic # classic tables -python3 scripts/export_autosketch_eval_table.py --synthetic --samples-per-sec 10 --keep-duplicate-queries \ - --revision beacf68 --out $TMP/rest # all, multigroup -# copy synthetic-templates{classic,all,multigroup}-shared1.json to $DIR/inputs/tables/ # SAT: the saturation dir of ../autosketch-vs-asap-hydra/README.md, "Reproduce" +python3 scripts/export_autosketch_eval_table.py --synthetic --samples-per-sec 10 \ + --revision acc2db1 --out $TMP for t in classic all multigroup; do f=synthetic-templates$t-shared1 + cp $TMP/$f.json $DIR/inputs/tables/ target/release/examples/autosketch_vs_asap synthetic --table $DIR/inputs/tables/$f.json \ --target p95 --runs 3 --no-chosen --saturation-dir $SAT --out $DIR/$f-tp95.json target/release/examples/autosketch_vs_asap synthetic --table $DIR/inputs/tables/$f.json \ @@ -104,8 +126,5 @@ for t in classic all multigroup; do done ``` -Without `--samples-per-sec`, the export writes the 200/s tables of -`../autosketch-vs-asap-hydra/inputs/tables/` byte for byte (but the final -newline pre-commit adds). Runs: 2026-10-13, -idle clnode109, runner at sketch-bench `eval/drop-group-rate-templates` -(4dbc20d). +Runs: 2026-10-14, sketch-bench `main` (acc2db1), on a 16-core desktop (not an +idle cluster node), so `planning_secs` are indicative only. diff --git a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json b/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json index ac973a72..ae0d3bbd 100644 --- a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json +++ b/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json @@ -1 +1 @@ -{"inputs": {}, "plan": "ProjectASAP/ASAPQuery#777", "schema_version": 1, "sketch_bench_revision": "beacf68", "workloads": [{"dataset": "synthetic/templates=all/shared=1", "e2e": {"metrics": {"data": {"labels": [{"cardinality": 10, "name": "job"}, {"cardinality": 1000, "child_of": "job", "name": "label_0"}], "samples_per_sec_per_series": 10, "type": "gauge", "values": {"dist": {"pareto": 2.0}, "value": {"keys": 10000, "zipf": 1.1}}}, "data_total": {"labels": [{"cardinality": 10, "name": "job"}, {"cardinality": 1000, "child_of": "job", "name": "label_0"}], "samples_per_sec_per_series": 10, "type": "counter", "values": {"increment": {"keys": 10000, "zipf": 1.1}}}, "flows": {"labels": [{"cardinality": 1000, "name": "dst_subnet", "skew": 1.1}, {"cardinality": 1000, "name": "dst_port", "skew": 1.2}, {"cardinality": 3, "name": "proto", "skew": 0.0}], "records_per_sec": 200000, "type": "records", "values": {"src_ip": {"burst": {"keys": 10000, "label": "dst_subnet", "share": 0.05, "value": 999}, "keys": 1000000, "zipf": 0.0}}}, "http": {"labels": [{"cardinality": 4, "name": "region", "skew": 0.5}, {"cardinality": 25, "name": "service", "skew": 1.1}, {"cardinality": 25, "child_of": "service", "name": "endpoint", "skew": 1.1}, {"cardinality": 4, "name": "status", "skew": 2.0}], "records_per_sec": 100000, "type": "records", "values": {"latency": {"anomaly": {"factor": 10, "label": "endpoint"}, "pareto": 2.0, "scaled_by": "service"}, "user_id": {"keys": 1000000, "zipf": 0.8}}}}, "queries": [{"interval_secs": 1, "promql": "sum by (job) (data)", "rqes": ["synthetic/templates=all/shared=1/sum_by_job/keys/1s"]}, {"interval_secs": 1, "promql": "quantile by (job) (0.5, data)", "rqes": ["synthetic/templates=all/shared=1/quantile_by_job_p0.5/values/1s"]}, {"interval_secs": 1, "promql": "quantile by (job) (0.75, data)", "rqes": ["synthetic/templates=all/shared=1/quantile_by_job_p0.75/values/1s"]}, {"interval_secs": 1, "promql": "quantile by (job) (0.9, data)", "rqes": ["synthetic/templates=all/shared=1/quantile_by_job_p0.9/values/1s"]}, {"interval_secs": 1, "promql": "quantile by (job) (0.95, data)", "rqes": ["synthetic/templates=all/shared=1/quantile_by_job_p0.95/values/1s"]}, {"interval_secs": 1, "promql": "quantile by (job) (0.99, data)", "rqes": ["synthetic/templates=all/shared=1/quantile_by_job_p0.99/values/1s"]}, {"interval_secs": 60, "promql": "topk(32, sum_over_time(data[15m]))", "rqes": ["synthetic/templates=all/shared=1/topk32_sum_by_label0_sum_over_time/keys/15m"]}, {"interval_secs": 60, "promql": "sum_over_time(data[15m])", "rqes": ["synthetic/templates=all/shared=1/sum_over_time/keys/15m"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.5, data[15m])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/15m"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.75, data[15m])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.75/values/15m"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[15m])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/15m"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.95, data[15m])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.95/values/15m"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.99, data[15m])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.99/values/15m"]}, {"interval_secs": 60, "promql": "rate(data_total[15m])", "rqes": ["synthetic/templates=all/shared=1/rate/keys/15m"]}, {"interval_secs": 60, "promql": "sum by (job) (sum_over_time(data[15m]))", "rqes": ["synthetic/templates=all/shared=1/sum_by_job_sum_over_time/keys/15m"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[15m]) / quantile_over_time(0.5, data[15m])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/15m", "synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/15m"]}, {"interval_secs": 60, "promql": "topk(32, sum_over_time(data[1h]))", "rqes": ["synthetic/templates=all/shared=1/topk32_sum_by_label0_sum_over_time/keys/1h"]}, {"interval_secs": 60, "promql": "sum_over_time(data[1h])", "rqes": ["synthetic/templates=all/shared=1/sum_over_time/keys/1h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.5, data[1h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/1h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.75, data[1h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.75/values/1h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[1h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/1h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.95, data[1h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.95/values/1h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.99, data[1h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.99/values/1h"]}, {"interval_secs": 60, "promql": "rate(data_total[1h])", "rqes": ["synthetic/templates=all/shared=1/rate/keys/1h"]}, {"interval_secs": 60, "promql": "sum by (job) (sum_over_time(data[1h]))", "rqes": ["synthetic/templates=all/shared=1/sum_by_job_sum_over_time/keys/1h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[1h]) / quantile_over_time(0.5, data[1h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/1h", "synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/1h"]}, {"interval_secs": 60, "promql": "topk(32, sum_over_time(data[6h]))", "rqes": ["synthetic/templates=all/shared=1/topk32_sum_by_label0_sum_over_time/keys/6h"]}, {"interval_secs": 60, "promql": "sum_over_time(data[6h])", "rqes": ["synthetic/templates=all/shared=1/sum_over_time/keys/6h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.5, data[6h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/6h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.75, data[6h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.75/values/6h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[6h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/6h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.95, data[6h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.95/values/6h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.99, data[6h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.99/values/6h"]}, {"interval_secs": 60, "promql": "rate(data_total[6h])", "rqes": ["synthetic/templates=all/shared=1/rate/keys/6h"]}, {"interval_secs": 60, "promql": "sum by (job) (sum_over_time(data[6h]))", "rqes": ["synthetic/templates=all/shared=1/sum_by_job_sum_over_time/keys/6h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[6h]) / quantile_over_time(0.5, data[6h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/6h", "synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/6h"]}, {"interval_secs": 60, "promql": "topk(32, sum_over_time(data[24h]))", "rqes": ["synthetic/templates=all/shared=1/topk32_sum_by_label0_sum_over_time/keys/24h"]}, {"interval_secs": 60, "promql": "sum_over_time(data[24h])", "rqes": ["synthetic/templates=all/shared=1/sum_over_time/keys/24h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.5, data[24h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/24h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.75, data[24h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.75/values/24h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[24h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/24h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.95, data[24h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.95/values/24h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.99, data[24h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.99/values/24h"]}, {"interval_secs": 60, "promql": "rate(data_total[24h])", "rqes": ["synthetic/templates=all/shared=1/rate/keys/24h"]}, {"interval_secs": 60, "promql": "sum by (job) (sum_over_time(data[24h]))", "rqes": ["synthetic/templates=all/shared=1/sum_by_job_sum_over_time/keys/24h"]}, {"interval_secs": 60, "promql": "quantile_over_time(0.9, data[24h]) / quantile_over_time(0.5, data[24h])", "rqes": ["synthetic/templates=all/shared=1/quantile_over_time_p0.9/values/24h", "synthetic/templates=all/shared=1/quantile_over_time_p0.5/values/24h"]}, {"capability": "cardinality", "grouping": ["dst_subnet"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t11_distinct_src_by_dst_subnet/keys/5m"], "sql": "SELECT dst_subnet, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_subnet", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "grouping": ["dst_subnet"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t11_distinct_src_by_dst_subnet/keys/15m"], "sql": "SELECT dst_subnet, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_subnet", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "grouping": ["dst_port"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t12_distinct_src_by_dst_port/keys/5m"], "sql": "SELECT dst_port, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_port", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "grouping": ["dst_port"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t12_distinct_src_by_dst_port/keys/15m"], "sql": "SELECT dst_port, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_port", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "grouping": ["dst_subnet", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/5m"], "sql": "SELECT dst_subnet, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_subnet, proto", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "grouping": ["dst_subnet", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/15m"], "sql": "SELECT dst_subnet, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_subnet, proto", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "grouping": ["dst_port", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=all/shared=1/t12_distinct_src_by_dst_port,proto/keys/5m"], "sql": "SELECT dst_port, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_port, proto", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", 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{"assumptions": [], "capability": "rate", "dataset": "synthetic/templates=classic/shared=1", "families": [{"family": "exact-increase", "grid_K": null, "grid_param": null, "sketch": "exact-increase", "target": 0.0}], "id": "synthetic/templates=classic/shared=1/rate/keys/24h", "instant": false, "interval_secs": 60, "kind": "keys", "label_set": {"arrival_rate_per_sec": 100000, "groups": 10000, "keys_per_window": null}, "lookback_secs": 86400, "max_N": 864000.0, "metric": "data", "min_N": 864000.0, "queries_per_instance": 1, "query_id": "rate", "range": "24h", "stream": "series/increment"}, {"assumptions": [], "capability": "sum", "dataset": "synthetic/templates=classic/shared=1", "families": [{"family": "exact-sum", "grid_K": null, "grid_param": null, "sketch": "exact-sum", "target": 0.0}], "id": "synthetic/templates=classic/shared=1/sum_by_job_sum_over_time/keys/24h", "instant": false, "interval_secs": 60, "kind": "keys", "label_set": {"arrival_rate_per_sec": 100000, "groups": 10, "keys_per_window": null}, "lookback_secs": 86400, "max_N": 864000000.0, "metric": "data", "min_N": 864000000.0, "queries_per_instance": 1, "query_id": "sum_by_job_sum_over_time", "range": "24h", "stream": "job/value"}]}]} diff --git a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesmultigroup-shared1.json b/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesmultigroup-shared1.json index a886122b..867a037c 100644 --- a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesmultigroup-shared1.json +++ b/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesmultigroup-shared1.json @@ -1 +1 @@ -{"inputs": {}, "plan": "ProjectASAP/ASAPQuery#777", "schema_version": 1, "sketch_bench_revision": "beacf68", "workloads": [{"dataset": "synthetic/templates=multigroup/shared=1", "e2e": {"metrics": {"flows": {"labels": [{"cardinality": 1000, "name": "dst_subnet", "skew": 1.1}, {"cardinality": 1000, "name": "dst_port", "skew": 1.2}, {"cardinality": 3, "name": "proto", "skew": 0.0}], "records_per_sec": 200000, "type": "records", "values": {"src_ip": {"burst": {"keys": 10000, "label": "dst_subnet", "share": 0.05, "value": 999}, "keys": 1000000, "zipf": 0.0}}}, "http": {"labels": [{"cardinality": 4, "name": "region", "skew": 0.5}, {"cardinality": 25, "name": "service", "skew": 1.1}, {"cardinality": 25, "child_of": "service", "name": "endpoint", "skew": 1.1}, {"cardinality": 4, "name": "status", "skew": 2.0}], "records_per_sec": 100000, "type": "records", "values": {"latency": {"anomaly": {"factor": 10, "label": "endpoint"}, "pareto": 2.0, "scaled_by": "service"}, "user_id": {"keys": 1000000, "zipf": 0.8}}}}, "queries": [{"capability": "cardinality", "grouping": ["dst_subnet"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t11_distinct_src_by_dst_subnet/keys/5m"], "sql": "SELECT dst_subnet, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY 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src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_port", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "grouping": ["dst_subnet", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/5m"], "sql": "SELECT dst_subnet, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_subnet, proto", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "grouping": ["dst_subnet", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/15m"], "sql": "SELECT dst_subnet, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_subnet, proto", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "grouping": ["dst_port", "proto"], 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"alpha": 0.01 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "data", - "params": { - "alpha": 0.02 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "data", - "params": { - "alpha": 0.05 + "alpha": 0.1 }, "sketch": "dd", "tail_index": 2.0, @@ -89,7 +69,7 @@ "distinct_keys": 0.0, "metric": "data", "params": { - "k": 200 + "k": 25 }, "sketch": "kll-percall", "tail_index": 2.0, @@ -129,27 +109,7 @@ "distinct_keys": 0.0, "metric": "http", "params": { - "alpha": 0.01 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "http", - "params": { - "alpha": 0.02 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "http", - "params": { - "alpha": 0.05 + "alpha": 0.1 }, "sketch": "dd", "tail_index": 2.0, @@ -179,7 +139,7 @@ "distinct_keys": 0.0, "metric": "http", "params": { - 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480.0, + "distinct_probes": 8, "probed_configs": [ { "distinct_keys": 10000.0, @@ -43,27 +43,7 @@ "distinct_keys": 0.0, "metric": "data", "params": { - "alpha": 0.01 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "data", - "params": { - "alpha": 0.02 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "data", - "params": { - "alpha": 0.05 + "alpha": 0.1 }, "sketch": "dd", "tail_index": 2.0, @@ -89,7 +69,7 @@ "distinct_keys": 0.0, "metric": "data", "params": { - "k": 200 + "k": 25 }, "sketch": "kll-percall", "tail_index": 2.0, @@ -106,8 +86,8 @@ "zipf_s": null } ], - "probes": 150, - "search_secs": 0.0014777 + "probes": 100, + "search_secs": 0.000583547 }, "cost_model": "w_cpu * AUC(CPU) + w_mem * AUC(memory), billed by use; latency = longest chain, reported", "dropped_unservable": { @@ -134,24 +114,24 @@ { "active_deployments": 14, "bound_ms": 20.77857286432161, - "cpu": 9.83460149701229, + "cpu": 9.83314509331592, "cpu_parts": { "compaction": 0.00020316471245114466, - "ingest": 9.828909495, - "query": 0.005488837299838979 + "ingest": 9.828180055, + "query": 0.004761873603469341 }, - "gib": 31.979439197669752, + "gib": 13.290785902184975, "gib_parts": { "compaction": 1.6470034141284517e-6, - "ingest": 9.266120068901149, - "query": 0.000049785584350797634, - "storage": 22.713267696180832 + "ingest": 4.6001815276080045, + "query": 0.000010709526686657407, + "storage": 8.690592018046871 }, "latency_ms": 20.778572864321614, "median_latency_ms": 2.28682287437186, "method": "asap", - "milp_solve_secs": 0.039835441, - "objective": 9.83460149701229, + "milp_solve_secs": 0.016977899, + "objective": 9.83314509331592, "rolled_up_rqes": 0, "rqes": 42, "weights": "cpu" @@ -159,49 +139,49 @@ { "active_deployments": 12, "bound_ms": 31.857907279929847, - "cpu": 4.889437160293372, + "cpu": 4.681123881041447, "cpu_parts": { "compaction": 0.00008836091568955893, - 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"workload": "synthetic/templates=classic/shared=1/tp95" -} \ No newline at end of file +} diff --git a/rqe-optimizer/results/e2e-rate10/synthetic-templatesmultigroup-shared1-tp95.json b/rqe-optimizer/results/e2e-rate10/synthetic-templatesmultigroup-shared1-tp95.json index 19bb03d2..f2e8902e 100644 --- a/rqe-optimizer/results/e2e-rate10/synthetic-templatesmultigroup-shared1-tp95.json +++ b/rqe-optimizer/results/e2e-rate10/synthetic-templatesmultigroup-shared1-tp95.json @@ -1,7 +1,7 @@ { "autosketch": { - "benchmark_secs_paper_rate": 540.0, - "distinct_probes": 9, + "benchmark_secs_paper_rate": 420.0, + "distinct_probes": 7, "probed_configs": [ { "distinct_keys": 1000000.0, @@ -27,27 +27,7 @@ "distinct_keys": 0.0, "metric": "http", "params": { - "alpha": 0.01 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "http", - "params": { - "alpha": 0.02 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "http", - "params": { - "alpha": 0.05 + "alpha": 0.1 }, "sketch": "dd", "tail_index": 2.0, @@ -77,7 +57,7 @@ "distinct_keys": 0.0, "metric": "http", "params": { - "k": 200 + "k": 25 }, "sketch": "kll-percall", "tail_index": 2.0, @@ -94,8 +74,8 @@ "zipf_s": null } ], - "probes": 156, - "search_secs": 0.002715991 + "probes": 112, + "search_secs": 0.000660478 }, "cost_model": "w_cpu * AUC(CPU) + w_mem * AUC(memory), billed by use; latency = longest chain, reported", "dropped_unservable": { @@ -122,2253 +102,2253 @@ { "active_deployments": 9, "bound_ms": 490.15722656250006, - "cpu": 0.09713686107599782, + "cpu": 0.09367972276273935, "cpu_parts": { "compaction": 0.0, - "ingest": 0.020548220000000002, - "query": 0.07658864107599782 + "ingest": 0.01880638, + "query": 0.07487334276273935 }, - "gib": 1.300399483952175, + "gib": 1.287884081512279, "gib_parts": { "compaction": 0.0, - "ingest": 0.8928343057632446, - "query": 0.0002800839277611149, - "storage": 0.40728509426116943 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