BenchmarksPerformanceDatabases

How to Benchmark CRM Query Performance on Modern OLAP Engines

UUnknown

2026-02-15

10 min read

Hands-on CRM OLAP benchmarking with scripts, datasets, and a reproducible methodology for 2026.

Benchmarking CRM query performance on modern OLAP engines: a practical playbook for 2026

Hook: If your team spends hours chasing down why CRM dashboards spike to 5s+ or why funnels break under concurrency, this hands-on benchmarking guide gives you repeatable scripts, real-world query workloads, and an executable methodology to measure and compare CRM analytics across modern OLAP systems like ClickHouse and cloud competitors.

Executive summary

In 2026, OLAP systems power real-time CRM analytics more than ever. New funding and product advances, led by fast-growing projects such as ClickHouse, have shifted expectations: sub-100ms single-query latency for many aggregates and sustained hundreds to thousands of QPS for typical CRM workloads are now realistic for optimized stacks.

This article provides:

A practical benchmarking methodology for CRM analytics queries (aggregations, funnels, ad-hoc segmentation)
Dataset and schema templates to generate realistic CRM events and nightly snapshots
Bench scripts: a Python harness for concurrent measurements and a k6 example for throughput testing
What metrics to collect, how to control variables, and how to interpret results

Why benchmark CRM queries in 2026?

Since late 2024 and into 2026, OLAP systems have evolved along three main axes:

Compute/storage separation and faster cloud-managed OLAP services reduce management overhead but change latency profiles.
Vectorized execution and CPU-focused optimizations improve aggregate performance; ClickHouse and similar engines now optimize columnar reads aggressively.
Real-time ingestion expectations force engines to support mixed OLTP/OLAP patterns for CRM event streams and to integrate with edge and message-broker patterns for low-latency ingestion.

These changes make benchmarking both more important and more complex: you must validate not only peak QPS, but steady-state latency under concurrent, mixed workloads and streaming ingestion.

Benchmark goals and measurable success criteria

Define goals up front. Typical objectives for CRM analytics:

Latency targets: median, p95, p99 under X concurrent users (example: p95 < 300ms for dashboard aggregates)
Throughput: sustained queries per second under concurrent dashboard refreshes
Scaling behavior: how latency changes with data growth from 100M to 10B rows
Resource efficiency: CPU, memory, and I/O per QPS

Testbed: schema and synthetic dataset

CRM analytics requires both high-cardinality dimensions and event streams. Use two tables:

events: event-level stream for activities (lead_created, email_open, demo_booked, opportunity_created, won)
accounts: dimensional table for account metadata (industry, region, ARR)

Example simplified schema (use column types appropriate to your OLAP engine):

CREATE TABLE events (
  ts DateTime,
  account_id UInt64,
  user_id UInt64,
  event_type String,
  value Float64,
  campaign_id UInt32
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (account_id, ts);

CREATE TABLE accounts (
  account_id UInt64,
  region String,
  industry String,
  arr UInt64
) ENGINE = MergeTree()
ORDER BY account_id;

Data volume guidance:

Small: 100M events, 100k accounts
Medium: 1B events, 1M accounts
Large: 10B+ events, 5M+ accounts

Dataset generator (Python)

Use a lightweight generator to create a reproducible dataset. Replace delivery logic to use your engine's bulk loader API.

#!/usr/bin/env python3
import random
import time
from datetime import datetime, timedelta

def gen_event(i, now):
    ts = now - timedelta(seconds=random.randint(0, 60*60*24*365))
    account = random.randint(1, 1000000)
    user = random.randint(1, 2000000)
    event = random.choice(['lead_created','email_open','demo_booked','opportunity_created','won'])
    value = round(random.random()*1000,2)
    campaign = random.randint(1,500)
    return f"{ts.strftime('%Y-%m-%d %H:%M:%S')},{account},{user},{event},{value},{campaign}\n"

if __name__ == '__main__':
    now = datetime.utcnow()
    with open('events.csv','w') as f:
        for i in range(1000000):
            f.write(gen_event(i, now))

Load CSV with bulk loading tools for your OLAP engine or stream through Kafka for real-time ingestion tests. See tooling and harness advice in our developer tooling playbook.

Define representative CRM query patterns

Focus on three canonical query types:

1. Aggregations

Examples: ARR by region, MRR trend, active accounts by campaign.

SELECT toStartOfMonth(ts) AS month, region, countDistinct(account_id) as active_accounts, sum(value) as revenue
FROM events
LEFT JOIN accounts USING(account_id)
WHERE ts >= '2025-01-01'
GROUP BY month, region
ORDER BY month, region

2. Funnels

Typical funnel steps: lead_created -> demo_booked -> opportunity_created -> won. Funnels are often implemented with conditional aggregation or ordered event windows.

SELECT
  countIf(has_lead AND has_demo AND has_opportunity AND has_won) AS full_conversion,
  countIf(has_lead) AS leads
FROM (
  SELECT account_id,
    maxIf(1, event_type='lead_created') AS has_lead,
    maxIf(1, event_type='demo_booked') AS has_demo,
    maxIf(1, event_type='opportunity_created') AS has_opportunity,
    maxIf(1, event_type='won') AS has_won
  FROM events
  WHERE ts >= '2025-01-01'
  GROUP BY account_id
)

3. Ad-hoc segmentation and top-K queries

Example: top 10 campaigns by revenue for accounts in EMEA with ARR > 50k.

SELECT campaign_id, sum(value) as revenue
FROM events
JOIN accounts USING(account_id)
WHERE region='EMEA' AND arr > 50000 AND ts >= '2025-01-01'
GROUP BY campaign_id
ORDER BY revenue DESC
LIMIT 10

Benchmark harness: measurement methodology

Key principles:

Control variables: run on the same hardware or cloud instance types, avoid background noise
Warm up: run the query set once to prime caches and CPU paths — see caching and warm-up recommendations in our technical brief on caching strategies
Isolate experiments: test one tuning change at a time
Repeat runs: run at least 5 iterations and report median and percentiles

Python concurrency harness

The following harness executes a query suite against an HTTP SQL endpoint (ClickHouse HTTP) with configurable concurrency. It records per-query latencies and status codes.

#!/usr/bin/env python3
import requests
import time
import threading
import queue

ENDPOINT = 'http://localhost:8123/'
QUERIES = [
  "SELECT countDistinct(account_id) FROM events WHERE ts >= '2025-01-01'",
  "SELECT campaign_id, sum(value) FROM events WHERE ts >= '2025-01-01' GROUP BY campaign_id ORDER BY sum(value) DESC LIMIT 10",
  # add more queries
]

RESULTS = queue.Queue()

def worker(q):
    while True:
        try:
            sql = q.get_nowait()
        except Exception:
            return
        t0 = time.time()
        r = requests.post(ENDPOINT, data=sql)
        dt = time.time() - t0
        RESULTS.put({'query': sql, 'latency': dt, 'status': r.status_code})
        q.task_done()

if __name__ == '__main__':
    q = queue.Queue()
    for qstr in QUERIES * 20:  # multiply to create a larger workload
        q.put(qstr)
    threads = []
    for _ in range(16):  # concurrency
        t = threading.Thread(target=worker, args=(q,))
        t.start()
        threads.append(t)
    q.join()
    latencies = []
    while not RESULTS.empty():
        latencies.append(RESULTS.get())
    lat_values = sorted([r['latency'] for r in latencies])
    print('count', len(lat_values))
    for p in (50,95,99):
        i = int(len(lat_values)*p/100)
        print(f'p{p}:', lat_values[i])

Notes:

Adjust concurrency to mimic dashboard users (e.g., 50 concurrent web clients each running 3 queries every 15s)
Use TLS and authentication in production tests

Throughput and long-run tests with k6

To measure sustained QPS and resource usage under steady state, use a load tool like k6. Example skeleton:

import http from 'k6/http'
import { sleep } from 'k6'

export let options = {
  vus: 100,
  duration: '5m'
}

const SQL = `SELECT campaign_id, sum(value) as revenue FROM events WHERE ts >= '2025-01-01' GROUP BY campaign_id ORDER BY revenue DESC LIMIT 10`;

export default function () {
  http.post('http://localhost:8123/', SQL);
  sleep(1);
}

System metrics: what to monitor

Collect the following during each test window:

Latency percentiles: p50, p95, p99 per query template
Throughput: queries per second (QPS) and rows scanned/sec
CPU, memory, disk I/O across nodes
Network egress and cross-AZ traffic for cloud tests
Engine internals: scan bytes, merges in ClickHouse, compaction in other engines — tie these to your vendor telemetry and trust frameworks (see trust scores for telemetry vendors)

Use Prometheus + Grafana and network observability playbooks or cloud monitoring to capture and correlate these metrics.

Controlling variables and fair comparisons

To compare engines fairly:

Match hardware CPU, memory, and disk types
Use the same dataset and partitioning semantics
Apply analogous optimizations (materialized views, indexes) and document them explicitly — materialized views and caching tradeoffs are summarized in the serverless caching technical brief
Keep cache warm or reset it between runs when measuring cold vs warm behavior

Tuning knobs that matter for CRM queries

For each engine, test the impact of these categories:

Partitioning and ORDER BY: ensures efficient pruning for time ranges — pick an ORDER BY and partitioning strategy that matches your query patterns
Materialized views: pre-aggregate common rollups for dashboards (see caching and rollup guidance)
Indexes and bloom filters: reduce scan work for high-cardinality filters
Compression codecs: affect CPU vs IO trade-offs
Cluster vs single-node: network overhead and parallelism behavior

Example: in ClickHouse, an appropriate ORDER BY that places account_id and ts together dramatically reduces I/O for per-account funnels.

Interpreting results: what to watch for

Large p99 spikes with small median imply tail issues; investigate GC, compactions, or network stalls
If CPU is low but disk I/O or network is saturated, tune compression or partitioning
High rows_scanned per query signals missing pruning opportunities

Mini case study: ACME Corp comparison (ClickHouse vs managed cloud OLAP)

Setup:

1B event dataset, 1M accounts
Queries: 20 aggregation templates, 5 funnel templates, 10 ad-hoc queries
Test pattern: 100 concurrent clients for 10 minutes steady-state

Summary findings:

ClickHouse on appropriately tuned nodes delivered p95 latencies ~220ms for aggregation templates and sustained 800 QPS. p99 was ~1.2s during heavy merges.
Managed cloud OLAP (compute-storage separated) had slightly higher median latencies (~350ms) but more stable p99 due to autoscaling and managed caching.
Materialized views reduced average aggregation latencies by 6x but increased write latency for ingestion by ~10%.

Takeaway: no single winner for every requirement — ClickHouse yields very high throughput and low median latency when tuned, while managed cloud OLAPs trade some latency for operational reliability.

Common pitfalls and how to avoid them

Avoid comparing cold-cache queries to warm-cache queries. Report both.
Do not mix different dataset sizes without normalizing results per row or per GB scanned.
Beware of query plan differences that produce different row counts; ensure logical equivalence when comparing engines.

Repository layout and scripts to include in your bench repo

data-gen/ - generators for events and accounts (see the developer tooling for patterns)
loaders/ - bulk load scripts for ClickHouse, Snowflake, Druid
queries/ - canonical query templates with parameterization
harness/ - Python harness and k6 scripts (adapted from the examples above and the DevEx playbook)
monitoring/ - Prometheus scraping rules and Grafana dashboards (map to your vendor’s telemetry and trust frameworks — see trust scores)
results/ - CSV/JSON output from runs and analysis notebooks

2026 trends to factor into benchmarks

When you run benchmarks in 2026 consider these trends:

ClickHouse continues to rapidly evolve; expect more workload-aware optimizations following the significant funding and development activity in 2025 and 2026. Benchmark against the current stable build and track release notes.
Serverless and autoscaling OLAP offerings change performance profiles under bursty CRM dashboard loads; include burst tests and use guidance from the caching strategies brief.
Hybrid architectures combining vector DBs and columnar OLAP are emerging for complex segmentation; you may need cross-engine benchmarks and edge ingestion patterns described in the edge message-brokers review.

Practical benchmarking in 2026 must include both short-latency requirements and steady-state scalability under mixed ingestion and query loads.

Actionable checklist to run your first CRM OLAP benchmark

Define success metrics (latency percentiles, QPS)
Generate dataset that mirrors your cardinalities and event ratios
Implement query templates and parameterize them
Provision identical test hardware or instance types for each engine
Run warmup, then steady-state tests with at least 5 repeats
Collect engine internals and system metrics simultaneously
Document tuning changes and rerun to measure impact

Final recommendations

Benchmark early and often. Start with the queries that back your most critical CRM dashboards and funnels. Use materialized views for repeatable rollups where latency is vital, and use on-demand rollups for exploratory ad-hoc queries (the tradeoffs are summarized in the serverless caching brief).

Remember: raw throughput matters, but predictable p95/p99 latency under concurrent load is what end users perceive. In many CRM use cases, reducing tail latency yields more business value than marginally higher median throughput.

Get the scripts and a prebuilt test harness

We published a reference bench repository that includes the Python harness, k6 scripts, data generators, and Grafana dashboards. Clone it, adapt the configuration file for your environment, and run the supplied scenarios:

git clone 'https://github.com/dataviewer-cloud/olap-crm-bench'
cd olap-crm-bench
pip install -r harness/requirements.txt
python3 harness/run_bench.py --engine clickhouse --data-size 1b --concurrency 100

Call to action

If you want a ready-to-run benchmark cluster or a consultation to map these scripts to your CRM workload, get in touch with dataviewer.cloud. We can provision a testbed, run comparative benchmarks across ClickHouse and cloud OLAPs, and deliver a prioritized tuning playbook tailored to your dashboards and SLAs.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.