Benchmarking Transaction Patterns

Guides users through benchmarking, explaining, and comparing two formulations of the same transactional business workflow in CockroachDB: explicit multi-statement transactions versus single-statement CTE transactions. Focuses on performance under contention, fair test methodology, and result interpretation.

Complement to design skills: For general transaction design principles, see designing-application-transactions. For SQL syntax and query patterns, see cockroachdb-sql.

When to Use This Skill

• Comparing explicit multi-statement transactions versus CTE-based single-statement transactions
• Benchmarking CockroachDB workloads under high concurrency or hot-key contention
• Investigating retry pressure, p95/p99 latency, or throughput differences between transaction formulations
• Deciding whether to rewrite a multi-step application flow into a single SQL statement
• Setting up a fair side-by-side benchmark with proper reset discipline
• Interpreting benchmark results (throughput, retries, tail latency, failures)
• Explaining why SQL Activity still shows waiting even with CTE transactions

Prerequisites

• CockroachDB test cluster (do not benchmark on production)
• SQL client or JDBC driver for benchmark execution
• Understanding of CockroachDB SERIALIZABLE isolation and retry behavior
• Familiarity with basic concurrency testing concepts

Core Concept

When two implementations perform the same business behavior, the transaction formulation itself can be a primary performance lever under contention.

Explicit Transaction Model

The application orchestrates the workflow as separate SQL statements inside a transaction: read state, apply logic, write changes, commit.

BEGIN;

SELECT balance FROM accounts WHERE id = $1;

-- Application decides whether transfer is allowed

UPDATE accounts SET balance = balance - $2 WHERE id = $1;
UPDATE accounts SET balance = balance + $2 WHERE id = $3;

INSERT INTO transfers (from_acct, to_acct, amount, created_at)
VALUES ($1, $3, $2, now());

COMMIT;

This keeps the transaction open across multiple statements and often includes application-side decision logic between steps.

CTE Transaction Model

The same read/decision/write logic is expressed as a single SQL statement, so the database evaluates and applies the business operation atomically without intermediate client orchestration.

WITH debit AS (
  UPDATE accounts
  SET balance = balance - $2
  WHERE id = $1
    AND balance >= $2
  RETURNING id
), credit AS (
  UPDATE accounts
  SET balance = balance + $2
  WHERE id = $3
    AND EXISTS (SELECT 1 FROM debit)
  RETURNING id
), ins AS (
  INSERT INTO transfers (from_acct, to_acct, amount, created_at)
  SELECT $1, $3, $2, now()
  WHERE EXISTS (SELECT 1 FROM debit)
    AND EXISTS (SELECT 1 FROM credit)
  RETURNING id
)
SELECT id FROM ins;

Why CTE Tends to Win Under Contention

The explicit version keeps the transaction open across multiple statements, increasing the time window for write conflicts, timestamp pushes, and retries. Under high concurrency, each retry repeats the read and write work and continues contending for the same hot data.

The CTE version collapses the same business logic into a single atomic statement, reducing transaction duration and sharply narrowing the contention window.

Steps

1. Prepare the Benchmark Environment

Set up a dedicated test database and schema. Do not mix benchmark workloads with other traffic.

CREATE DATABASE IF NOT EXISTS bankbench;
USE bankbench;

CREATE TABLE accounts (
  id INT PRIMARY KEY,
  balance DECIMAL(18,2) NOT NULL DEFAULT 0
);

CREATE TABLE transfers (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  from_acct INT NOT NULL,
  to_acct INT NOT NULL,
  amount DECIMAL(18,2) NOT NULL,
  created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

2. Seed the Test Data

Use multi-row UPSERT for efficient seeding. Single-row inserts distort setup cost.

INSERT INTO accounts (id, balance)
SELECT generate_series(1, 10000), 1000.00
ON CONFLICT (id) DO UPDATE SET balance = 1000.00;

3. Run the Explicit Transaction Benchmark

Execute with realistic concurrency (e.g., 64-128 workers) and a fixed duration or iteration count. Record throughput, retries, p50/p95/p99 latency, max latency, and failures.

4. Reset Between Runs for Fair Comparison

For a fair benchmark, reset account balances between explicit and CTE runs so table size, index size, and account state remain comparable.

UPDATE accounts SET balance = 1000.00;

5. Run the CTE Transaction Benchmark

Execute with the same concurrency, duration, and parameters as the explicit run.

6. Compare Results

Always compare these metrics side by side:

Metric	What to Look For
Throughput (txn/s)	Higher is better; CTE typically sustains better under contention
Total retries	CTE often reduces to near-zero
p50 latency	Median transaction time
p95 latency	Tail latency under moderate contention
p99 latency	Worst-case tail; explicit model often shows spikes
Max latency	Outlier behavior
Failures	Non-retryable errors

Benchmark Reference Results

In a reported high-contention run comparing the two models:

Metric	Explicit	CTE	Change
Throughput	591.1 txn/s	1,035.1 txn/s	+75.1%
Wall time	216.5s	123.7s	-42.9%
Average latency	202.2 ms	111.3 ms	-45.0%
Total retries	2,270,977	0	-100%

Extended runs preserved the same directional result at higher total volume, with the explicit model continuing to accumulate retries and occasional failures while the CTE model stayed at zero retries and zero failures.

Impact Summary

Dimension	Explicit Multi-Statement	Single-Statement CTE
Round trips	Multiple client/server interactions	Single request
Transaction lifetime	Longer	Shorter
Client retry complexity	Higher	Lower
Atomic invariant enforcement	Spread across statements/app logic	Contained in SQL
Expected throughput	Lower under contention	Higher under contention
Client-visible retries	More likely	Often reduced

Decision Guidance

Prefer the Explicit Pattern When

• The business workflow truly cannot be expressed cleanly in one SQL statement
• Readability or staged business logic matters more than peak throughput
• The contention level is low enough that retry amplification is not the dominant cost

Prefer the CTE Pattern When

• The workflow is contention-heavy
• The operation is naturally atomic
• The application currently performs read-decide-write across multiple statements
• The main goal is higher throughput, lower retries, and more stable p95/p99 latency

Fair Benchmark Rules

1. Reset between runs for fair comparison so balances, table size, and index size stay consistent
2. Treat no-reset runs as a demo, not an apples-to-apples benchmark
3. Use --batch-size=1 when you want one business unit of work at a time for clean comparison
4. Compare the right metrics — always include throughput, retries, p50, p95, p99, max latency, and failures
5. Use multi-row UPSERT for seeding — single-row seeding distorts setup cost

Common Misconceptions

"CTE always wins in every workload" — No. The claim is narrower: when the same business workflow can be expressed as a single atomic statement and the workload is contention-sensitive, collapsing the transaction shape can materially improve performance and stability.

"SQL Activity showing waiting means CTE failed" — Single-statement CTE execution does not eliminate contention. Statements can still wait on row conflicts, write intents, latches, or scheduling. The right comparison is overall throughput, tail latency, and retry profile.

"Single-statement means no contention" — A CTE can still wait under contention. The benefit is a narrower contention window, not the elimination of contention.

Safety Considerations

• Run benchmarks on dedicated test clusters, not production
• Reset data between runs for fair comparison
• Monitor cluster health during benchmark execution
• Use realistic but not destructive concurrency levels
• Validate that benchmark results transfer to your specific workload before making production changes

References

• CockroachDB Transactions Documentation
• Advanced Client-Side Transaction Retries
• Performance Best Practices
• Comparing Multi-Statement vs Single-Statement Transactions
• Set-Based Operations with CockroachDB
• Deep Dive into Transaction Retry Failures
• Troubleshooting CockroachDB Performance
• CockroachDB Transaction Demo
• CockroachDB Best Practices & Anti-Patterns Demo -- Demos 1-2 show retry patterns and contention scaling under concurrency