ClickHouse

ClickHouse analytics.
One conversation.

Connect to your ClickHouse cluster and build real-time dashboards, log analysis, and scheduled reports — just by asking.

events_log — ClickHouse
2026-03-13 14:02:31.847INFOapi-gatewayGET /v1/events 200 12ms
2026-03-13 14:02:31.923INFOauth-servicetoken validated user_id=8847291
2026-03-13 14:02:32.104WARNpayment-svcretry attempt 2/3 for charge ch_3N8x...
2026-03-13 14:02:32.218INFOapi-gatewayPOST /v1/events/batch 200 45ms (2,847 rows)
2026-03-13 14:02:32.445ERRORsearch-svctimeout after 5000ms query_id=q-9f2e...
2026-03-13 14:02:32.671INFOapi-gatewayGET /v1/dashboards/prod-metrics 200 8ms
2026-03-13 14:02:32.889INFOuser-serviceprofile updated user_id=9912034
2026-03-13 14:02:33.012INFOingest-workerbatch committed 14,291 events in 0.3s

Speed you can feel.

2.3B
rows scanned
0.8s
query time
1
message to dashboard
no row limit

How it works

1
clickhouse://default:***@clust...
Connected

Connect your cluster

Paste your connection string. Works with ClickHouse Cloud, self-hosted, or Altinity.

2

Show me error rates by service in the last hour

querying cluster...
Queried 2.3B rows in 0.8s

Ask in plain English

Type a question. camelAI writes optimized SQL and queries your cluster directly.

3
Requests / sec
Live

Last updated: 2s ago

Real-time dashboards

Get live dashboards that auto-refresh. Share them with your team in one click.

What analytics engineers build with camelAI.

Real-time dashboards

Monitor requests per second, error rates, and latency across services. camelAI builds live-refreshing dashboards you can share with your team — no Grafana configuration required.

Log analysis

Search and visualize billions of log entries. Find errors, trace request flows across services, and spot anomalies — all in one conversation. Like grep, but it understands your question.

Event analytics

Funnel analysis, user paths, conversion metrics — on your clickstream data. Get Sankey diagrams and cohort breakdowns without writing a single GROUP BY.

Scheduled reports

Set up cron jobs that query fresh ClickHouse data on a schedule, generate updated reports, and alert you in Slack when metrics cross a threshold.

From query to dashboard in one message.

Service Monitoring — ProductionLive
Requests / sec
12,847
▲ 12% vs yesterday
P99 Latency
142ms
▼ 8% vs yesterday
Error Rate
0.03%
── stable
Throughput
2.4 GB/s
▲ 5% vs yesterday
Requests per Second (last 24h)
15k10k5k0
00:0004:0008:0012:0016:0020:00

Built for teams who run on real-time data

Search logs...
14:02:31INFOapi-gatewayGET /v1/events 200 12ms
14:02:32WARNpayment-svcretry attempt 2/3
14:02:32ERRORsearch-svctimeout after 5000ms
14:02:33INFOingest-workerbatch committed 14,291 events
14:02:33ERRORsearch-svcconnection pool exhausted
errors/min+340%

Real-time log analysis

Stream logs from your ClickHouse cluster and spot issues in seconds. Search, filter, and visualize error patterns — no Kibana or Grafana setup required.

2.3Bevents
Pageviews 847K
Signups 524K
Activation 297K
Purchase 153K

Product analytics at scale

Analyze billions of user events without waiting for batch jobs. Build funnels, cohort analyses, and engagement dashboards that query live ClickHouse data.

What's the p99 latency for the auth service?

servicep50p99
auth-svc12ms142ms
api-gw8ms89ms
search-svc45ms380ms
0.8s

Ad-hoc exploration

Skip writing SQL. Ask questions in English and get instant results from your ClickHouse data. Perfect for incident investigations and quick data checks.

CPU

72%

Memory

64%12.8/20 GB

Req/s

12.8K

Errors

23

▼ 12%vs 1h ago

Ops dashboards

Build operational dashboards that your infrastructure team actually wants to use. Monitor system health, track SLAs, and alert on anomalies — all from ClickHouse data.

Frequently asked questions

What will you build on your ClickHouse data?

Connect to our ClickHouse cluster and build a real-time dashboard showing requests per second, p99 latency, and error rates by service. Refresh every 5 minutes via cron.

Try this prompt

Query the events table — 2.3 billion rows — and show me a funnel analysis: page_view → signup → first_purchase → repeat_purchase, broken down by acquisition source.

Try this prompt

Analyze our clickstream data from the last 7 days. Find the top 10 user paths through the product and visualize them as a Sankey diagram.

Try this prompt

Pull hourly error counts from our ClickHouse logs for the past month. Highlight anomalies and set up a cron job that alerts me in Slack when error rates spike.

Try this prompt

Your data is already fast.

Now make it useful.