driftbench-db 0.1.0b7

Database drift benchmarking for researchers, DB vendors, and new users: generate, validate, and run data/workload drift with CLI or MCP.

DriftBench

DriftBench is a toolkit for generating and replaying data drift and workload drift with DriftSpec.

This README is intentionally focused on how to use the latest DriftBench.

Version-by-version updates and service coverage:

• CHANGELOG.md

Who typically uses DriftBench:

• Researcher: design reproducible drift experiments and ablations.
• Database Vendor / Performance Team: run drift regression checks across targets before release.
• New User: start from validated examples and get first outputs quickly.

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Web Frontend

• Production site: driftbench.com
• Frontend source repo: driftbench-web
• Release branch note: pushes to release/** with user-facing DriftBench changes auto-dispatch a docs update event to driftbench-web.
• Dispatch verification note (2026-05-10): this README line is used to validate cross-repo release notifications.
• Dispatch verification note (retry): confirms the receiver workflow on driftbench-web is active after workflow fix.

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Release Reproducibility

• Workflow: .github/workflows/reproducible-drift-runs.yml
• Trigger manually from GitHub Actions (workflow_dispatch) or call from other workflows (workflow_call).
• Default run executes and validates:
  • driftspec/examples/demo_data_single.yaml
  • driftspec/examples/workload_census.yaml
• Artifacts are uploaded as driftbench-reproducible-run-artifacts.

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Install (Latest)

From PyPI (recommended)

python3 -m pip install -U driftbench-db

From source (latest main)

git clone https://github.com/Liuguanli/DriftBench.git
cd DriftBench
python3 -m pip install -e .

Verify installation

driftbench --help
driftbench-service --help
driftbench-mcp --help

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CLI Quickstart

Use this flow for most users:

# 1) Validate a DriftSpec
python -m driftbench.cli validate-spec driftspec/examples/demo_data_single.yaml --json

# 2) Preview execution plan
python -m driftbench.cli dry-run driftspec/examples/demo_data_single.yaml --json

# 3) Execute
python -m driftbench.cli run-yaml driftspec/examples/demo_data_single.yaml

# 4) Inspect outputs
python -m driftbench.cli list-outputs --root output --glob "**/*" --limit 30 --json

Trace to DriftSpec

python -m driftbench.cli trace-to-spec \
  driftspec/trace_inputs/trace_data_mock.csv \
  driftspec/generated/from_trace.yaml \
  --trace-type data

Orchestrate Across Benchmark Targets (MVP)

Use one DriftSpec across multiple benchmark targets defined in benchmark_target.yaml.

python -m driftbench.cli orchestrate \
  --spec driftspec/examples/demo_data_single.yaml \
  --targets driftspec/examples/adapters/benchmark_targets_mvp.yaml \
  --manifest-out output/orchestrate_manifest.json \
  --json

Execute setup/run commands for each target:

python -m driftbench.cli orchestrate \
  --spec driftspec/examples/demo_data_single.yaml \
  --targets driftspec/examples/adapters/benchmark_targets_mvp.yaml \
  --manifest-out output/orchestrate_manifest.json \
  --execute \
  --json

Bootstrap Dataset (download/copy + checksum + schema extract)

Bootstrap from preset, local path, or URL:

python -m driftbench.cli bootstrap dataset \
  --source census_original \
  --output-dir output/bootstrap/datasets \
  --json

With checksum verification:

python -m driftbench.cli bootstrap dataset \
  --source /path/to/my_dataset.csv \
  --output-dir output/bootstrap/datasets \
  --checksum sha256:<hex> \
  --json

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MCP Quickstart

Start MCP server (stdio):

python3 -m driftbench_mcp.server

Client config template:

• docs/mcp_config_example.json

Minimal MCP guide:

• docs/p0_mcp_server_minimal.md

Core MCP workflow:

1. trace_to_spec
2. validate_spec
3. run_spec
4. list_outputs

Spec sharing tools:

• save_spec
• list_public_specs
• import_spec_and_run

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MCP Chat Demo (Codex / Claude Code)

After MCP is configured, the best pattern is to give your assistant a case type plus what change you want to simulate.

Case A: Data Drift (data changes)

Use when you care about data size/distribution changes (scaling, skew, outliers, updates).

[Prompt: Data Drift]
Read docs/p0_integration_quickstart.md.
I want a DATA drift case on <my dataset path>.
Goal: <e.g., scale 2x + stronger skew on column amount>.
Please use MCP tools to:
1) build a DriftSpec (or trace_to_spec if needed),
2) validate it,
3) run it,
4) list outputs.
Then summarize what data files were generated and what changed.

Case B: Workload Drift (query changes)

Use when you care about query behavior changes (predicate distribution, selectivity, structure, payload).

[Prompt: Workload Drift]
I want a WORKLOAD drift case.
Query goal: <e.g., predicates shift from uniform to city-focused, selectivity from 10% to 60%>.
Please create/run a spec via MCP and report:
- generated workload files,
- how query distribution/selectivity changed,
- suggested next workload variant.

Temporal Overlay (applied on top of Case A or B)

Temporal drift is usually an overlay, not a standalone base case. Use it to add time evolution (uniform / periodic / trend / long-tail) on top of data drift or workload drift.

[Prompt: Temporal Overlay]
Take my <DATA or WORKLOAD> drift case and add TEMPORAL pattern <uniform|periodic|trend|long_tail>.
Please run the MCP workflow and summarize:
1) generated spec path,
2) output artifacts,
3) expected temporal behavior in plain language,
4) how temporal behavior changes the base (data/workload) case.

What users should expect

1. The assistant executes MCP tools in order (trace_to_spec/build_spec -> validate_spec -> run_spec -> list_outputs).
2. You get concrete artifact paths (generated YAML + output files).
3. You get a short interpretation of what changed for your selected case (data/query), plus temporal overlay effects when requested.
4. You usually get one or two suggested next iterations for deeper benchmarking.

Python API (Stable Entry Points)

Use top-level APIs instead of internal modules:

from driftbench import run_spec, trace_to_spec, get_schema_extractor

run_spec("driftspec/examples/demo_data_single.yaml")
trace_to_spec("driftspec/trace_inputs/trace_data_mock.csv", "driftspec/generated/from_trace.yaml")

Benchmark Objects (driftbench.data.xxx)

Use benchmark-specific objects to generate artifacts into a user-chosen directory. Seven benchmark adapters are available — see docs/benchmark_reference.md for full data/query details.

Adapter	Type	Tables	Queries
tpch	OLAP	8	22 templates
tpcds	OLAP / Decision support	4 (synth) / 26 (full)	Templates
tpcc	OLTP	9	5 transaction types
tpcc_skew	OLTP + access skew	9 + weight manifest	5 transaction types
job	OLAP / join-order	8 (synth) / 21 (full IMDB)	20 representative
ycsb	Key-value	1	6 workload mixes
dsb	Decision support	Configurable	Templates

1) Choose an output directory

output_dir is required. DriftBench will write files only under this directory.

2) Generate data and queries

from pathlib import Path
from driftbench.data.tpch import data as tpch_data, queries as tpch_queries
from driftbench.data.tpcds import data as tpcds_data, queries as tpcds_queries
from driftbench.data.tpcc import data as tpcc_data, queries as tpcc_queries
from driftbench.data.tpcc_skew import data as tpcc_skew_data, queries as tpcc_skew_queries
from driftbench.data.job import data as job_data, queries as job_queries
from driftbench.data.ycsb import data as ycsb_data, queries as ycsb_queries
from driftbench.data.dsb import data as dsb_data, queries as dsb_queries

out = Path("./artifacts")

# TPC-H (OLAP)
tpch_data(scale_factor=1).generate(output_dir=out)
tpch_queries(query_ids=[1, 3, 5], queries_per_template=2, mode="qgen").generate(output_dir=out)
# TPC-C (OLTP, scale_factor == number of warehouses)
tpcc_data(scale_factor=4).generate(output_dir=out)
tpcc_queries().generate(output_dir=out)

# TPC-C Skew (OLTP with Zipf hot-warehouse drift)
tpcc_skew_data(scale_factor=10, hot_warehouse_fraction=0.2, skew_factor=0.99).generate(output_dir=out)
tpcc_skew_queries(scale_factor=10, hot_warehouse_fraction=0.2).generate(output_dir=out)

# JOB — Join Order Benchmark (IMDB, join-order sensitivity)
job_data(scale_factor=1).generate(output_dir=out)
job_queries().generate(output_dir=out)

# YCSB (key-value workloads A–F)
ycsb_data(scale_factor=1).generate(output_dir=out)
ycsb_queries(workload="B").generate(output_dir=out)

# TPC-DS and DSB
tpcds_data(scale_factor=10).generate(output_dir=out)
tpcds_queries().generate(output_dir=out)
dsb_data(scale_factor=10).generate(output_dir=out)
dsb_queries().generate(output_dir=out)

3) Find generated files

Artifacts are written to:

<output_dir>/
  tpch/data/    tpch/queries/
  tpcds/data/   tpcds/queries/
  tpcc/data/    tpcc/queries/
  tpcc_skew/data/  tpcc_skew/queries/
  job/data/     job/queries/
  ycsb/data/    ycsb/queries/
  dsb/data/     dsb/queries/

Each generation creates a manifest (*_manifest.json) in its folder.
Use the manifest files field to see exactly which files were generated.

4) Programmatic path retrieval

generate() returns a GenerationResult with:

• result.files: generated file paths
• result.metadata: manifest path

This is the recommended way to chain into downstream benchmarking scripts.

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Where to find examples

• Example specs: driftspec/examples/
• Trace inputs: driftspec/trace_inputs/
• Integration tests with runnable fixtures: test/fixtures/specs/

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Core docs

• API boundary: docs/p0_api_boundary_freeze.md
• CLI/MCP command matrix: docs/p0_mcp_command_matrix.md
• Integration quickstart: docs/p0_integration_quickstart.md
• MCP examples script: docs/p0_mcp_examples.sh
• Release branch/tag policy: docs/release_branch_policy.md

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Testing

Run all tests:

python3 -m unittest discover -s test -p 'test_*.py' -v

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License

MIT (see LICENSE).