mz-benchmark

name

mz-benchmark

description

Add, modify, or debug benchmark scenarios for measuring Materialize performance. Covers three frameworks: Feature Benchmark (single-operation micro-benchmarks), Scalability Test (SQL throughput under concurrency), and Parallel Benchmark (sustained latency over time via scenarios.py). Trigger on "benchmark", "feature benchmark", "scalability test", "parallel benchmark", "performance regression", "micro-benchmark", "TPS", "latency test", or when editing files in feature_benchmark/scenarios/, scalability/workload/workloads/, or parallel_benchmark/scenarios.py. Note: this is about benchmark measurement frameworks, not the parallel-workload stress-testing framework (which tests for panics under concurrency, not performance).

Benchmark Frameworks

Materialize has some benchmark frameworks targetting local Docker, each suited to different performance concerns. Choose the right one based on what you're measuring.

Decision Guide

Concern	Framework	Best For
Single-operation latency	Feature Benchmark	"How fast is this SELECT / INSERT / CREATE INDEX?"
SQL throughput under concurrency	Scalability Test	"How many QPS can we sustain at 1/4/16/64/256 concurrent clients?"
Sustained performance over time	Parallel Benchmark	"Does latency degrade over a 2-minute mixed workload?"

---

1. Feature Benchmark (Micro-benchmarks)

Measures wall-clock time of individual SQL operations without concurrency. Uses testdrive fragments. Great for regression-testing specific code paths.

How to Add a Scenario

1. Add a class in one of the scenarios/*.py files. Extend Scenario (default, runs in CI), ScenarioBig (longer, opt-in), or ScenarioDisabled.
2. Implement benchmark() returning a Td(...) with timing markers (/* A */ and /* B */).
3. Optionally implement shared(), init(), before() if setup is required.

Scenario Template

from materialize.feature_benchmark.action import TdAction
from materialize.feature_benchmark.measurement_source import MeasurementSource, Td
from materialize.feature_benchmark.scenario import Scenario

class MyCategory(Scenario):
    """Group parent - won't run (not a leaf)."""
    pass

class MySpecificBenchmark(MyCategory):
    """Leaf class - this runs."""

    def init(self) -> list[TdAction]:
        return [
            self.table_ten(),  # Creates table 'ten' with values 0-9
            TdAction("""
                > CREATE TABLE t1 (f1 INTEGER);
                > INSERT INTO t1 SELECT {unique_values} FROM {join};
                > SELECT COUNT(*) FROM t1;
                {n}
            """.format(
                unique_values=self.unique_values(),
                join=self.join(),
                n=self.n(),
            )),
        ]

    def benchmark(self) -> MeasurementSource:
        return Td("""
> SELECT 1;
  /* A */
1

> SELECT COUNT(*) FROM t1 WHERE f1 % 2 = 0;
  /* B */
{half_n}
""".format(half_n=self.n() // 2))

Timing Markers

• /* A */ - Start marker. Measurement begins AFTER this completes.
• /* B */ - End marker. Measurement ends when this completes.
• Measures: time from end of A to end of B.
• For a single query, use SELECT 1; /* A */ as a dummy start to not measure the overhead of Testdrive starting up.

Scenario Phases (execution order)

1. shared() - Runs once for BOTH instances (e.g., create Kafka topics)
2. init() - Runs once per instance (create tables, populate data)
3. before() - Runs before EACH iteration (restart, cleanup)
4. benchmark() - Runs repeatedly (10-50 times), measured

Helpers

• self.n() - Returns 10^SCALE (default SCALE=6 → 1,000,000)
• self.table_ten() / self.view_ten() - Creates table/view with values 0-9
• self.unique_values() - SQL expression generating N unique values via cartesian product
• self.join() - SQL FROM clause for cartesian product joins
• self.keyschema() / self.schema() - Kafka schema helpers

Tips

• Aim for ~1 second query time. Too fast = noise; too slow = Unbearably slow CI.
• Test td fragments locally with standalone testdrive before running the full benchmark.
• Use SCALE to control data volume: SCALE = 5 → 100K rows.
• Use FIXED_SCALE = True if --scale should not affect your scenario. This is usually done when a scenario doesn't scale well, add an explanation.
• Use RELATIVE_THRESHOLD to customize regression sensitivity per scenario.

Running

bin/mzcompose --find feature-benchmark run default --root-scenario=MyCategory
bin/mzcompose --find feature-benchmark run default --root-scenario=MySpecificBenchmark

---

2. Scalability Test (SQL Throughput Under Concurrency)

Measures TPS (transactions per second) at increasing concurrency levels (1, 2, 4, 8, ..., 256 clients). Open-loop benchmark that identifies scaling bottlenecks. Compares Materialize against Postgres.

How to Add a Workload

1. Create a new file or add to an existing file in workload/workloads/.
2. Extend a marker class (DmlDqlWorkload, DdlWorkload, or ConnectionWorkload).
3. Implement operations() returning a list of Operation objects.

Workload Template (Simple DML/DQL)

from materialize.scalability.operation.operations.operations import (
    SelectOne,
    InsertDefaultValues,
    SelectCountInMv,
)
from materialize.scalability.workload.workload_markers import DmlDqlWorkload

class MyWorkload(DmlDqlWorkload):
    def operations(self) -> list["Operation"]:
        return [InsertDefaultValues(), SelectCountInMv()]

Workload Template (DDL with Data Exchange)

from materialize.scalability.operation.operation_data import OperationData
from materialize.scalability.operation.operations.operations import (
    CreateTableX, PopulateTableX, SelectStarFromTableX, DropTableX,
)
from materialize.scalability.operation.scalability_operation import OperationChainWithDataExchange
from materialize.scalability.workload.workload import WorkloadWithContext
from materialize.scalability.workload.workload_markers import DdlWorkload

class MyDdlWorkload(WorkloadWithContext, DdlWorkload):
    def amend_data_before_execution(self, data: OperationData) -> None:
        data.push("table_seed", data.get("worker_id"))

    def operations(self) -> list["Operation"]:
        return [
            OperationChainWithDataExchange([
                CreateTableX(), PopulateTableX(), SelectStarFromTableX(), DropTableX(),
            ])
        ]

Custom Operations

from materialize.scalability.operation.scalability_operation import SimpleSqlOperation

class MyCustomSelect(SimpleSqlOperation):
    def sql_statement(self) -> str:
        return "SELECT COUNT(*) FROM t1 WHERE f1 > 500;"

Built-in Operations

SelectOne, SelectStar, SelectLimit, SelectCount, SelectCountInMv, SelectUnionAll, Update, InsertDefaultValues, CreateTableX, PopulateTableX, DropTableX, CreateMvOnTableX, DropMvOfTableX, Connect, Disconnect.

Database Schema (auto-created)

For each workload, the framework creates: CREATE TABLE t1 (f1 INTEGER DEFAULT 1), inserts a row, and creates CREATE MATERIALIZED VIEW mv1 AS SELECT count(*) AS count FROM t1.

Running

bin/mzcompose --find scalability run default --workload MyWorkload
bin/mzcompose --find scalability run default --workload-group-marker DmlDqlWorkload
bin/mzcompose --find scalability run default --min-concurrency 1 --max-concurrency 128

---

3. Parallel Benchmark (Sustained Performance)

Measures latency distributions (p50/p95/p99) and QPS over a sustained load period (default 120s). Supports mixed workloads with both open-loop and closed-loop concurrency. Detects latency degradation over time via slope analysis.

How to Add a Scenario

1. Add a class in scenarios.py extending Scenario.
2. In __init__, call self.init(phases=[...]) with TdPhase (setup) and LoadPhase (benchmark).

Scenario Template

from materialize.parallel_benchmark.framework import (
    ClosedLoop,
    LoadPhase,
    OpenLoop,
    Periodic,
    ReuseConnQuery,
    Scenario,
    StandaloneQuery,
    TdPhase,
)

class MyScenario(Scenario):
    def __init__(self, c, conn_infos):
        self.init(
            phases=[
                # Setup phase (testdrive)
                TdPhase("""
                    > CREATE TABLE bench_t (f1 INTEGER);
                    > INSERT INTO bench_t SELECT generate_series(1, 10000);
                    > CREATE MATERIALIZED VIEW bench_mv AS
                      SELECT COUNT(*) AS c FROM bench_t;
                    > SELECT c FROM bench_mv;
                    10000
                """),
                # Benchmark phase
                LoadPhase(
                    duration=120,  # seconds
                    actions=[
                        OpenLoop(
                            action=StandaloneQuery(
                                "SELECT c FROM bench_mv",
                                conn_infos["materialized"],
                            ),
                            dist=Periodic(per_second=100),
                            report_regressions=True,
                        ),
                        ClosedLoop(
                            action=ReuseConnQuery(
                                "INSERT INTO bench_t VALUES (1)",
                                conn_infos["materialized"],
                            ),
                            report_regressions=True,
                        ),
                    ],
                ),
            ],
            guarantees={
                "SELECT c FROM bench_mv (standalone)": {
                    "qps": 10,
                    "p99": 500,
                },
            },
        )

Action Types

Class	Behavior	Use When
StandaloneQuery	New connection per operation	Testing connection overhead + query
ReuseConnQuery	Persistent single connection	Testing pure query latency
PooledQuery	Draws from connection pool	Testing pooled throughput (set conn_pool_size > 0)

Load Patterns

Class	Behavior
OpenLoop(dist=Periodic(per_second=N))	Fixed-rate: N operations/second regardless of latency
OpenLoop(dist=Gaussian(mean, stddev))	Variable-rate: sleep drawn from gaussian distribution
ClosedLoop(...)	Sequential: next operation starts when previous completes

Guarantees and Regression Thresholds

# Absolute guarantees (fail if not met)
guarantees={
    "SELECT 1 (standalone)": {"qps": 15, "p99": 400, "max": 500},
}

# Relative thresholds (fail if regression vs baseline)
regression_thresholds={
    "SELECT 1 (pooled)": {"qps": 0.8, "p99": 1.3},  # QPS must be >=80% of baseline
}

Action names are auto-generated as "{query} ({connection_type})".

Available Metrics

queries, qps, min, max, avg, p50, p95, p99, p99.9, ..., p99.999999, std, slope.

Disabling a Scenario

from materialize.parallel_benchmark.framework import disabled

@disabled("Reason for disabling")
class MyScenario(Scenario):
    ...

Running

bin/mzcompose --find parallel-benchmark run default --scenario MyScenario
bin/mzcompose --find parallel-benchmark run default --scenario MyScenario --load-phase-duration 60