diff --git a/rqe-optimizer/data/autosketch-eval/README.md b/rqe-optimizer/data/autosketch-eval/README.md new file mode 100644 index 00000000..ddf7bde0 --- /dev/null +++ b/rqe-optimizer/data/autosketch-eval/README.md @@ -0,0 +1,103 @@ +# AutoSketch vs ASAPQuery evaluation table + +`table.json` holds the accuracy and cost evidence for the AutoSketch vs +ASAPQuery planner evaluation (plan: ProjectASAP/ASAPQuery#777, section 6). It +is generated by `scripts/export_autosketch_eval_table.py`. + +## Contents + +Workloads (W2): one per dataset in ASAPQuery's +`asap-tools/dataset-analysis/results/skew_summary.csv`. + +| Dataset | RQEs | Config rows | Saturated rows | +|---|---|---|---| +| alibaba_v2022 | 51 | 1020 | 867 | +| boom | 20 | 140 | 47 | +| google_2011 | 27 | 546 | 403 | + +Each RQE is one (dataset, query_id, kind, range). Its data parameters are the +worst case over the **whole dataset** (`worst_theta_cms`, `worst_K`, `max_N`, +`worst_alpha_rank`/`worst_alpha_memory`), rounded to the measured grid toward +the harder side by `recommend_config.grid_point`. There is no burst variant: +these whole-dataset worst cases replace AutoSketch's synthetic burst injection. + +Each RQE lists every measured config of every candidate family (keys: CMS, +CountSketch, CMS-heap top-k; values: KLL, DDSketch), with: + +| Field | Meaning | +|---|---| +| `autosketch_error` | Single-sketch curve at `N = max_N`. AutoSketch keeps one sketch per query window and never merges, so it has no saturation requirement. | +| `asap_error_by_m` | Error after merging `m ∈ {1, 4, 16, 64}` shards, at `N = max_N`. CMS, CountSketch, HLL and DDSketch merge exactly: every `m` reads the single-sketch curve. KLL and top-k read the merged curve at `m` (sketch-bench #131); a missing one falls back to the single-sketch curve, flagged "single-sketch curve optimistic". | +| `saturated` | `max_N ≥ n_sat`. ASAP merges smaller-window sketches, so it may only use a config's value when this is true. | +| `n_sat`, `not_saturated_in_grid` | From `saturation.csv`; the 1e9 subset wins when it has the point. | +| `memory_bytes`, `insert_ns_per_item`, `merge_us_per_fold`, `query_phase_us` | `recommend_config.cost_of`. `query_phase_us` is the benchmark's whole query phase, not one query. | +| `*_flag*` | Grid and interpolation flags: "N below grid", "extrapolated beyond N=…", "cost scaled from rows=3". | + +Per family: `error_metric`, `higher_is_better`, `target` (from +skew_summary), and the grid point used. + +## Label sets and windows (assumptions) + +`rqe-optimizer` needs `card(ℓ)` (sketch instances) and `λ(ℓ)` (items/s). +skew_summary.csv describes one population per query, so: + +- **`groups = 1`.** A key query (`sum by (k)`) is one sketch whose keys are + the group-by values (`keys_per_window = worst_K`). It is not one sketch per + group. Value queries have no `by`. +- **`arrival_rate_per_sec = max_N / lookback_secs`**: the rate in the worst + window. +- **Instant queries:** `lookback_secs = step_s`, because `max_N` counts one + step's samples. +- **BOOM** has no timestamps, window or N in skew_summary, so + `boom_query()` derives them from its 20 equal chunks per series: + - `N = rows_total / 20`; + - `lookback = interval` = one chunk's duration, from the sampling step in + the series name (`ds-2187-H` is hourly); + - a series without an alpha fit uses the hardest grid alpha for error and + the easiest for memory. + + Each RQE's `assumptions` field lists what was assumed. + +## Regenerate + +The raw curves (2.8 MB) are not committed. Produce them with +`scripts/study_saturation.py`: the grid from #130 (`--phase all`), the 1e9 +subset, and `--merge-shards-list 1,4,16,64` from #131 for top-k and KLL/DDSketch. +Then run: + +```sh +python3 scripts/export_autosketch_eval_table.py \ + --summary /asap-tools/dataset-analysis/results/skew_summary.csv \ + --curves out_grid_1e7_cost/saturation_curve.csv \ + --curves-big out_1e9/saturation_curve.csv \ + --saturation out_grid_1e7_cost/saturation.csv \ + --saturation-big out_1e9/saturation.csv \ + --merge-curves out_merge_topk/saturation_merge_curve.csv \ + out_merge_quantile/saturation_merge_curve.csv \ + --revision "curves: sketch-bench #130 study_saturation.py; merge curves: sketch-bench #131 (branch merged-accuracy)" \ + --out rqe-optimizer/data/autosketch-eval/table.json +``` + +The merge curves were produced on #131's branch at a0b8b42. #131 has since +merged into main as bd644fe with identical content, so the recorded revision +string above still describes the inputs. + +`table.json` records the SHA-256 of every input under `inputs`: + +| Input | SHA-256 | +|---|---| +| skew_summary.csv (ASAPQuery 46f3942) | `f5984b655e7720449fdf081dd4c0a8d883b5bf2a9fd968f379fa8d44df364382` | +| out_grid_1e7_cost/saturation_curve.csv | `2d34a925c5bf8e4e72cab5932f63f8ec6e9cdf3e57b07f21aae2e5192144ca1a` | +| out_grid_1e7_cost/saturation.csv | `569941ea162698963671d2a0ad40faeff53614d3cdb374de2a3b8aa3917ae674` | +| out_1e9/saturation_curve.csv | `fb8fc62a16e137b831c20c16fe8d9b846e3fe80eb468451f08a727514d1e2bfd` | +| out_1e9/saturation.csv | `0dd8448349f63317d7f4d385ce3063ab78d8daf3504015f580b7147cd22a3f22` | +| out_merge_topk/saturation_merge_curve.csv | `ec43995c8b9ac0862fe5c19f038693fbce6e73cf48ebf46fb3bacbfb474d4ef6` | +| out_merge_quantile/saturation_merge_curve.csv | `d4fb544bf14b06274f2558632faa766b5601ba4f942b48119f45c04aa39898f8` | + +## Limits + +- Merged curves cover `N ≤ 1e7` and `m ≤ 64`. A deployment with `m > 64` + has no measured KLL/top-k value. +- 530 config rows extrapolate beyond the largest measured N, and 56 rows + sit below the grid's smallest N; both are flagged. +- 720 rows=5 configs take 5/3 of the rows=3 cost (flagged). diff --git a/rqe-optimizer/data/autosketch-eval/table.json b/rqe-optimizer/data/autosketch-eval/table.json new file mode 100644 index 00000000..98f852bf --- /dev/null +++ b/rqe-optimizer/data/autosketch-eval/table.json @@ -0,0 +1,54796 @@ +{ + "inputs": { + "out_1e9/saturation.csv": "0dd8448349f63317d7f4d385ce3063ab78d8daf3504015f580b7147cd22a3f22", + "out_1e9/saturation_curve.csv": "fb8fc62a16e137b831c20c16fe8d9b846e3fe80eb468451f08a727514d1e2bfd", + "out_grid_1e7_cost/saturation.csv": "569941ea162698963671d2a0ad40faeff53614d3cdb374de2a3b8aa3917ae674", + "out_grid_1e7_cost/saturation_curve.csv": "2d34a925c5bf8e4e72cab5932f63f8ec6e9cdf3e57b07f21aae2e5192144ca1a", + "out_merge_quantile/saturation_merge_curve.csv": 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"autosketch_error": 0.0037676761127085397, + "autosketch_flag": "", + "config": "alpha=0.005", + "cost_flag": "", + "insert_ns_per_item": 15.6329, + "memory_bytes": 9224.0, + "merge_us_per_fold": 1.8444444444444446, + "n_sat": 1778.0, + "not_saturated_in_grid": false, + "params": { + "alpha": 0.005 + }, + "query_phase_us": 2017.3333333333333, + "saturated": true + } + ], + "error_metric": "mean_rank_err", + "family": "dd", + "flags": [ + "alpha 16.0911 beyond grid, used 3", + "light tail" + ], + "grid_K": "", + "grid_param": 3.0, + "higher_is_better": false, + "sketch": "dd", + "target": 0.01 + } + ], + "id": "google_2011/memory_p99/values/1h", + "instant": false, + "interval_secs": 300.0, + "kind": "values", + "label_set": { + "arrival_rate_per_sec": 679.6536111111111, + "groups": 1, + "keys_per_window": null + }, + "lookback_secs": 3600.0, + "max_N": 2446753.0, + "min_N": 1188516.0, + "query_id": "memory_p99", + "range": "1h" + } + ] + } + ] +} diff --git a/scripts/export_autosketch_eval_table.py b/scripts/export_autosketch_eval_table.py new file mode 100644 index 00000000..08092466 --- /dev/null +++ b/scripts/export_autosketch_eval_table.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +"""Export the accuracy/cost table for the AutoSketch vs ASAPQuery evaluation. + +Plan: ProjectASAP/ASAPQuery#777 (docs/evaluation/autosketch-vs-planner.md, +section 6 "Benchmark input"). One RQE per (dataset, query_id, kind, range) of +ASAPQuery's dataset-analysis skew_summary.csv, worst case over the whole +dataset, rounded to the measured grid exactly as recommend_config.py does. +Every config of every candidate family is listed (no pick), with two lookups: + + autosketch_error the single-sketch curve at N = max_N. AutoSketch keeps + one sketch per query window and never merges, so it has + no saturation requirement. + asap_error_by_m the error after merging m = 1, 4, 16, 64 shards, at + N = max_N. CMS, CountSketch, HLL and DDSketch merge + exactly, so every m reads the single-sketch curve. KLL and + top-k read the merged curve at m (sketch-bench #131) when + one is given, else the single-sketch curve, flagged. + ASAP merges smaller-window sketches, so it may only use a + value when `saturated` (max_N >= n_sat). + +Costs are recommend_config.py's (memory_bytes, insert_ns_per_item, +merge_us_per_fold, query_phase_us). There is no burst variant: the worst case +over the whole dataset replaces AutoSketch's synthetic burst injection. + +BOOM rows have no timestamps, window or N in skew_summary.csv; boom_query() +derives them from the 20 equal chunks per series and records the assumptions. +""" + +import argparse +import csv +import hashlib +import json +import math +import os +import re +import subprocess +import sys + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from recommend_config import ( # noqa: E402 + DEFAULT_TARGETS, FAMILIES, KIND_FAMILIES, MERGE_INEXACT, cost_of, error_at, grid_point, + load_merge_curves, load_saturation, nominal_size, num, read_summary) +from study_saturation import load_curves # noqa: E402 + +SHARD_COUNTS = [1, 4, 16, 64] +CAPABILITY = {"cms": "freq", "countsketch": "freq", "topk": "topk", + "kll": "quantile", "dd": "quantile"} + + +def params_of(config): + """'rows=3 cols=1024' -> {"rows": 3, "cols": 1024}.""" + out = {} + for part in config.split(): + k, v = part.split("=") + out[k] = int(v) if v.isdigit() else float(v) + return out + + +def saturation_of(srow, n): + """(n_sat or None, saturated): saturated when n >= n_sat.""" + if not srow or srow["n_sat"] == "not_saturated": + return None, False + n_sat = num(srow["n_sat"]) + return n_sat, n_sat is not None and n >= n_sat + + +def config_row(q, family, key, cost_key, curves, curves_big, sat, sat_big, merge): + """One config's lookups for query-range q, or None without a curve.""" + curve = curves.get(key) + if not curve: + return None + n = q["max_N"] + if n > curve[-1][0] and key in curves_big: + curve = curves_big[key] + single, single_flag = error_at(curve, n) + by_m, m_flags = {}, {} + for m in SHARD_COUNTS: + if m == 1 or family not in MERGE_INEXACT: + by_m[str(m)], m_flags[str(m)] = single, single_flag + continue + merged = merge.get(key, {}).get(m) + if merged: + by_m[str(m)], m_flags[str(m)] = error_at(merged, n) + else: + by_m[str(m)] = single + m_flags[str(m)] = "; ".join( + x for x in ["no merge curve; single-sketch curve optimistic", single_flag] if x) + srow = sat_big.get(key) or sat.get(key) + n_sat, saturated = saturation_of(srow, n) + cost = cost_of(sat, cost_key, curves[key][-1][0]) + memory, insert_ns, merge_us, query_us = (None if x == "" else x for x in cost[:4]) + cost_flag = cost[4] + return { + "config": key[1], "params": params_of(key[1]), + "memory_bytes": memory, "insert_ns_per_item": insert_ns, + "merge_us_per_fold": merge_us, "query_phase_us": query_us, + "cost_flag": cost_flag, + "n_sat": n_sat, "not_saturated_in_grid": bool(srow) and srow["n_sat"] == "not_saturated", + "saturated": saturated, + "autosketch_error": single, "autosketch_flag": single_flag, + "asap_error_by_m": by_m, "asap_flag_by_m": m_flags, + } + + +BOOM_CHUNKS = 20 +STEP_UNIT_SECS = {"S": 1, "T": 60, "H": 3600, "D": 86400} + + +def boom_query(r): + """A read_summary-shaped query for one BOOM series row. + + One evaluation is one of the series' 20 equal chunks over all variates: + N = rows_total / 20, lookback = interval = the chunk's duration, from the + series' sampling step in its name (ds-2187-H: hourly). A series without an + alpha fit (too few rows, or every variate light) uses the hardest grid + alpha for the error and the easiest for memory, flagged by grid_point. + """ + match = re.search(r"-(\d*)([SDHT])\]$", r["query_id"]) + step = int(match.group(1) or 1) * STEP_UNIT_SECS[match.group(2)] + rows, variates = num(r["rows_total"]), num(r["K_total"]) + n = rows / BOOM_CHUNKS + span = round(rows / variates / BOOM_CHUNKS * step) + alpha_rank, alpha_memory = num(r["worst_alpha_rank"]), num(r["worst_alpha_memory"]) + assumptions = [f"N = rows_total / {BOOM_CHUNKS} chunks", + f"lookback = interval = one chunk ({rows / variates / BOOM_CHUNKS:g} " + f"steps of {step} s)"] + if alpha_rank is None: + alpha_rank, alpha_memory = math.inf, 0.0 + assumptions.append("no alpha fit: hardest grid alpha for error, easiest for memory") + q = {"dataset": r["dataset"], "query_id": r["query_id"], "kind": r["kind"], + "range": f"chunk_of_{BOOM_CHUNKS}", "range_s": span, "step_s": span, + "theta": None, "K": None, "min_N": n, "max_N": n, + "alpha_rank": alpha_rank, "alpha_memory": alpha_memory, + "tail_class": r.get("tail_class", ""), "assumptions": assumptions} + for t, default in DEFAULT_TARGETS.items(): + q[t] = default if num(r.get(t)) is None else num(r.get(t)) + return q + + +def queries(path): + """read_summary's queries, plus one per BOOM series (read_summary drops them).""" + out = read_summary(path) + with open(path, newline="") as f: + out += [boom_query(r) for r in csv.DictReader(f) if r["dataset"] == "boom"] + return out + + +def rqe_entry(q, curves, curves_big, sat, sat_big, merge): + """The table entry for one skew_summary query-range.""" + instant = not q["range_s"] + # An instant query reads one step's samples (max_N counts one step window). + lookback = q["step_s"] if instant else q["range_s"] + families = [] + for family in KIND_FAMILIES[q["kind"]]: + sketch, target_col, higher_better = FAMILIES[family] + points = [k for k in curves if k[0] == sketch] + if not points: + continue + param, cost_param, card, common = grid_point(q, points) + rows = [] + for config in sorted({k[1] for k in points}, key=nominal_size): + key = (sketch, config, points[0][2], float(param), card) + cost_key = (sketch, config, key[2], float(cost_param), card) + row = config_row(q, family, key, cost_key, curves, curves_big, sat, sat_big, merge) + if row: + rows.append(row) + if not rows: + continue + metric = (sat.get(key) or sat_big.get(key) or {}).get("error_metric", "") + families.append({ + "family": family, "capability": CAPABILITY[family], "sketch": sketch, + "error_metric": metric, "higher_is_better": higher_better, + "target": q[target_col], "grid_param": param, "grid_K": card, + "flags": [x for x in common if x], "configs": rows, + }) + return { + "id": f"{q['dataset']}/{q['query_id']}/{q['kind']}/{q['range']}", + "dataset": q["dataset"], "query_id": q["query_id"], "kind": q["kind"], + "range": q["range"], "instant": instant, + "lookback_secs": lookback, "interval_secs": q["step_s"], + "min_N": q["min_N"], "max_N": q["max_N"], + # One sketch per query: group-by values are the sketch's keys, not + # separate instances (README, "Label sets"). + "label_set": {"groups": 1, + "arrival_rate_per_sec": q["max_N"] / lookback if lookback else None, + "keys_per_window": q["K"]}, + "assumptions": q.get("assumptions", []) + ( + ["instant: lookback = one step (max_N counts one step window)"] if instant else []), + "families": families, + } + + +def sha256(path): + h = hashlib.sha256() + with open(path, "rb") as f: + for block in iter(lambda: f.read(1 << 20), b""): + h.update(block) + return h.hexdigest() + + +def build(args): + curves = load_curves(args.curves) + curves_big = load_curves(args.curves_big) if args.curves_big else {} + sat = load_saturation(args.saturation) + sat_big = load_saturation(args.saturation_big) + merge = {} + for path in args.merge_curves or []: + for key, per_m in load_merge_curves(path).items(): + merge.setdefault(key, {}).update(per_m) + workloads = {} + for q in queries(args.summary): + if not q["step_s"]: + continue + entry = rqe_entry(q, curves, curves_big, sat, sat_big, merge) + workloads.setdefault(q["dataset"], []).append(entry) + ids = [r["id"] for rqes in workloads.values() for r in rqes] + assert len(ids) == len(set(ids)), "RQE ids must be unique" + inputs = [p for p in [args.summary, args.curves, args.curves_big, args.saturation, + args.saturation_big, *(args.merge_curves or [])] if p] + return { + "schema_version": 1, + "plan": "ProjectASAP/ASAPQuery#777", + "shard_counts": SHARD_COUNTS, + "inputs": {os.path.basename(os.path.dirname(p)) + "/" + os.path.basename(p): sha256(p) + for p in inputs}, + "sketch_bench_revision": args.revision, + "workloads": [{"dataset": d, "rqes": r} for d, r in sorted(workloads.items())], + } + + +def main(argv=None): + parser = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--summary", required=True, help="ASAPQuery skew_summary.csv") + parser.add_argument("--curves", required=True) + parser.add_argument("--curves-big") + parser.add_argument("--saturation", required=True) + parser.add_argument("--saturation-big") + parser.add_argument("--merge-curves", nargs="*", help="saturation_merge_curve.csv files") + parser.add_argument("--revision", help="sketch-bench commit of the curves (default: HEAD)") + parser.add_argument("--out", required=True) + args = parser.parse_args(argv) + if args.revision is None: + args.revision = subprocess.run(["git", "rev-parse", "HEAD"], capture_output=True, + text=True, check=False).stdout.strip() + table = build(args) + os.makedirs(os.path.dirname(args.out) or ".", exist_ok=True) + with open(args.out, "w") as f: + json.dump(table, f, indent=1, sort_keys=True) + f.write("\n") + n = sum(len(w["rqes"]) for w in table["workloads"]) + print(f"Done. {n} RQEs in {args.out}", file=sys.stderr) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/recommend_config.py b/scripts/recommend_config.py index 4eb9883c..b873b0e1 100644 --- a/scripts/recommend_config.py +++ b/scripts/recommend_config.py @@ -240,6 +240,26 @@ def scaled(col, factor): scaled("query_cpu_secs", 1e6), flag) +def grid_point(q, points): + """(param, cost_param, card, flags): q's worst case rounded to the grid of + one sketch's measured points, toward the harder side.""" + common = [] + if q["kind"] == "keys": + param, f1 = round_down(q["theta"], {k[3] for k in points}) + card, f2 = round_up(q["K"], {int(k[4]) for k in points}) + cost_param, card = param, str(card) + common += [f"theta {f1}" if f1 else "", f"K {f2}" if f2 else ""] + else: + param, f1 = round_up(q["alpha_rank"], {k[3] for k in points}) + cost_param = q["alpha_memory"] if q["alpha_memory"] is not None else q["alpha_rank"] + cost_param, _ = round_down(cost_param, {k[3] for k in points}) + card = "" + common += [f"alpha {f1}" if f1 else ""] + if q["tail_class"] == "light": + common.append("light tail") + return param, cost_param, card, common + + def recommend(q, curves, curves_big, sat, sat_big, merge): """One output row per candidate family of query-range q.""" out = [] @@ -250,20 +270,7 @@ def recommend(q, curves, curves_big, sat, sat_big, merge): if not points: continue configs = sorted({k[1] for k in points}, key=nominal_size) - common = [] - if q["kind"] == "keys": - param, f1 = round_down(q["theta"], {k[3] for k in points}) - card, f2 = round_up(q["K"], {int(k[4]) for k in points}) - cost_param, card = param, str(card) - common += [f"theta {f1}" if f1 else "", f"K {f2}" if f2 else ""] - else: - param, f1 = round_up(q["alpha_rank"], {k[3] for k in points}) - cost_param = q["alpha_memory"] if q["alpha_memory"] is not None else q["alpha_rank"] - cost_param, _ = round_down(cost_param, {k[3] for k in points}) - card = "" - common += [f"alpha {f1}" if f1 else ""] - if q["tail_class"] == "light": - common.append("light tail") + param, cost_param, card, common = grid_point(q, points) n = q["max_N"] shards = 1 if q["range_s"] and q["step_s"]: diff --git a/scripts/test_export_autosketch_eval_table.py b/scripts/test_export_autosketch_eval_table.py new file mode 100644 index 00000000..983d797f --- /dev/null +++ b/scripts/test_export_autosketch_eval_table.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +"""Unit tests for export_autosketch_eval_table on tiny synthetic CSVs. + +Run: python3 scripts/test_export_autosketch_eval_table.py +""" + +import csv +import json +import os +import sys +import tempfile +import unittest + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from export_autosketch_eval_table import boom_query, main # noqa: E402 +from study_saturation import CURVE_COLUMNS, SUMMARY_COLUMNS # noqa: E402 + +SUMMARY_HEADER = [ + "dataset", "query_id", "promql", "kind", "weight", "range", "range_s", "step_s", + "K_total", "rows_total", "worst_theta_cms", "worst_K", "min_N", "max_N", + "worst_alpha_rank", "worst_alpha_memory", "target_are_top100", + "target_precision_at_k", "target_hll_rel_err", "target_rank_err", "tail_class", +] +CMS, KLL = "cms-fastpath-vector2d", "kll-percall" +MERGE_HEADER = CURVE_COLUMNS[:7] + ["shards"] + CURVE_COLUMNS[7:] + + +def write_csv(path, header, rows): + with open(path, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(header) + writer.writerows(rows) + + +class ExportTest(unittest.TestCase): + def setUp(self): + self.tmp = tempfile.TemporaryDirectory() + d = self.tmp.name + self.path = lambda name: os.path.join(d, name) + # One key query (CMS) and one value query (KLL), 1h lookback, 60 s step. + write_csv(self.path("summary.csv"), SUMMARY_HEADER, [ + ["d", "freq", "sum by (k) (x)", "keys", "count", "1h", "3600", "60", "", "", + "1.0", "1000", "500", "5000", "", "", "", "", "", "", ""], + ["d", "p99", "quantile(0.99, x)", "values", "", "1h", "3600", "60", "", "", + "", "", "500", "5000", "2.0", "2.0", "", "", "", "", ""], + ]) + curve = [(100, 0.5), (1000, 0.2), (10000, 0.1)] + write_csv(self.path("curve.csv"), CURVE_COLUMNS, [ + *[["frequency", CMS, "rows=3 cols=256", "zipf", "1.0", "1000", n, e, 0] + for n, e in curve], + *[["quantile", KLL, "k=50", "pareto", "2.0", "", n, e, 0] for n, e in curve], + ]) + # CMS saturates at 1000 <= max_N 5000; KLL saturates at 10000 > 5000. + write_csv(self.path("sat.csv"), SUMMARY_COLUMNS, [ + ["frequency", CMS, "rows=3 cols=256", "zipf", "1.0", "1000", "1000", "0.1", + "are_top100", "0.1", "0.001", "0.002", "3072"], + ["quantile", KLL, "k=50", "pareto", "2.0", "", "10000", "0.1", + "mean_rank_err", "0.1", "0.001", "0.002", "800"], + ]) + # Merged KLL curve only at m=4: doubles the error. + write_csv(self.path("merge.csv"), MERGE_HEADER, [ + ["quantile", KLL, "k=50", "pareto", "2.0", "", n, 4, 2 * e, 0] for n, e in curve]) + + def tearDown(self): + self.tmp.cleanup() + + def export(self): + out = self.path("table.json") + main(["--summary", self.path("summary.csv"), "--curves", self.path("curve.csv"), + "--saturation", self.path("sat.csv"), "--merge-curves", self.path("merge.csv"), + "--revision", "test", "--out", out]) + with open(out) as f: + table = json.load(f) + rqes = {r["query_id"]: r for w in table["workloads"] for r in w["rqes"]} + return {q: r["families"][0]["configs"][0] for q, r in rqes.items()}, rqes + + def test_exact_merge_reads_the_single_sketch_value_for_every_m(self): + configs, _ = self.export() + cms = configs["freq"] + self.assertEqual(set(cms["asap_error_by_m"].values()), {cms["autosketch_error"]}) + + def test_inexact_merge_reads_the_merged_curve_or_flags_its_absence(self): + configs, _ = self.export() + kll = configs["p99"] + self.assertAlmostEqual(kll["asap_error_by_m"]["4"], 2 * kll["asap_error_by_m"]["1"]) + self.assertEqual(kll["asap_error_by_m"]["16"], kll["autosketch_error"]) + self.assertIn("single-sketch curve optimistic", kll["asap_flag_by_m"]["16"]) + + def test_saturated_compares_max_N_with_n_sat(self): + configs, _ = self.export() + self.assertTrue(configs["freq"]["saturated"]) + self.assertFalse(configs["p99"]["saturated"]) + + def test_autosketch_reads_the_curve_even_when_unsaturated(self): + configs, _ = self.export() + # max_N 5000 lies between 1000 (0.2) and 10000 (0.1), log-linear: 0.130. + self.assertAlmostEqual(configs["p99"]["autosketch_error"], 0.2 - 0.1 * 0.69897, 4) + + def test_rqe_carries_window_and_label_set(self): + _, rqes = self.export() + r = rqes["freq"] + self.assertEqual((r["lookback_secs"], r["interval_secs"]), (3600, 60)) + self.assertEqual(r["label_set"]["groups"], 1) + self.assertAlmostEqual(r["label_set"]["arrival_rate_per_sec"], 5000 / 3600) + + +class BoomTest(unittest.TestCase): + def test_chunk_defines_n_and_window(self): + row = dict.fromkeys(SUMMARY_HEADER, "") + row.update(dataset="boom", query_id="target_tail[ds-1-5T]", kind="values", + K_total="10", rows_total="2000") + q = boom_query(row) + self.assertEqual(q["max_N"], 100) + # 2000 rows / 10 variates / 20 chunks = 10 steps of 300 s. + self.assertEqual((q["range_s"], q["step_s"]), (3000, 3000)) + self.assertEqual(q["alpha_rank"], float("inf")) + + +if __name__ == "__main__": + unittest.main()