migrationanalyticscosts

Practical Guide: Migrating Hosted Analytics to ClickHouse for Lower Query Costs

bbeek

2026-02-05

10 min read

Step-by-step playbook for hosting providers to migrate analytics to ClickHouse and cut query costs with schema, ingestion, and ops best practices.

Hook: Your analytics bill is exploding — here's the pragmatic playbook to stop it

If you run hosting infrastructure that serves analytics for customers, you already feel the squeeze: unpredictable cloud billing, slow OLAP queries that soak CPU, and fragmented tooling that wastes operators' time. Moving analytics workloads to ClickHouse is one of the fastest, highest-ROI levers available in 2026 for cutting query costs while improving latency. This guide gives hosting providers a step-by-step migration playbook: assessment, schema design, ingestion, query tuning, cost controls, and operations—so you can migrate with predictable cost savings and minimal risk.

Why ClickHouse — and why now (2025–2026 context)

ClickHouse has accelerated from niche OLAP engine to mainstream analytics platform. Late 2025 and early 2026 brought two clear signals: growing enterprise investment in ClickHouse (including a major funding round reported by Bloomberg), and rapid maturation of cloud-native tooling, managed ClickHouse offerings, and operators that make large-scale deployments operationally feasible for hosts.

"ClickHouse, a Snowflake challenger that offers an OLAP database management system, raised $400M led by Dragoneer at a $15B valuation" — Bloomberg, Jan 2026

Those developments matter because they mean the project has deepening ecosystem support and vendor-neutral momentum. For hosting providers, the practical payoff is real: ClickHouse's columnar engine, compact compression, and merge-tree storage model often reduce CPU and storage costs for analytics queries by 2–10x compared to generic row-store or heavyweight MPP alternatives—when applied with correct schema and operational controls.

What you’ll get from this playbook

Concrete, stage-by-stage migration steps for hosts and managed providers
Actionable schema and ingestion patterns that reduce query cost
Operational and monitoring best practices for 2026 (cloud-native, S3 tiering, Kubernetes operators)
Checklist, KPIs, and rollback safety nets to migrate without downtime

High-level migration strategy (inverted pyramid)

Start small, measure savings, then scale. The fastest path to immediate cost reduction is:

Identify a high-cost analytics workload (top 10% of queries by CPU)
Prototype using a representative subset of data and queries
Deploy ClickHouse alongside existing systems with dual-write or ETL
Iterate schema + queries until latency and cost targets are met
Roll traffic over and convert downstream consumers

Step 1 — Assessment: find the low-hanging fruit

Your migration should prioritize workloads with clear ROI. Use telemetry to find candidates:

Top queries by CPU and latency (Prometheus, New Relic, or your APM)
Large scans or high-cardinality group-bys that run frequently
Dashboards with heavy refresh rates and overlapping aggregation logic

Baseline metrics to capture:

Average and p95 query CPU-time and wall time
Bytes scanned per query
Monthly queries and cost per 1M queries

Run a quick cost model

Estimate expected CPU and storage savings conservatively. For example, if a workload consumes 2,000 vCPU-hours/month at $0.04/vCPU-hour, and ClickHouse reduces that by 70%, you save ~56% on compute alone. Add storage compression and tiering for more gains. Use historical query samples and simulate them against a ClickHouse prototype to get realistic numbers.

Step 2 — Choose deployment architecture

Hosting providers have a few practical deployment options:

Managed ClickHouse Cloud — fastest to adopt, vendor-managed, good SLAs; ideal when operational bandwidth is tight.
Self-hosted on VMs — full control, better at squeezing cost via custom instance types and local NVMe, but requires ops expertise.
Kubernetes operator (self-managed) — good balance for teams with K8s expertise; operators (official or community) handle shard/replica lifecycle.

Decide based on staffing, required SLAs, and whether you need multi-tenant isolation. For multi-tenant hosting, consider logical separation via databases and quota enforcement rather than spinning per-tenant clusters unless you offer dedicated clusters as a premium.

Step 3 — Schema design patterns that save CPU and IO

Schema is the single biggest lever for ClickHouse cost savings. ClickHouse is not a drop-in replacement for transactional row databases — design for reads.

Guiding principles

Denormalize aggressively: pre-join attributes you always query together.
Partition to enable fast data pruning and cheap deletes.
Order by the columns used in GROUP BY or range filters to minimize scanned data.
Use appropriate data types (Date, DateTime64, LowCardinality(String), Enum) to shrink memory and index sizes.
Pre-aggregate with materialized views or AggregatingMergeTree for repeatable heavy aggregations.

Example table design

Events table optimized for time-series analytics:

CREATE TABLE events
(
  event_time DateTime64(3),
  tenant_id UInt64,
  user_id UInt64,
  event_type LowCardinality(String),
  properties String,
  value Float64
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (tenant_id, event_time)
SETTINGS index_granularity = 8192;

Why this helps:

PARTITION BY enables dropping or compacting older months cheaply.
ORDER BY aligns on tenant_id+time, so queries filtered by tenant and time read small ranges.
LowCardinality reduces storage and speeds up string-heavy group-bys.

Use MergeTree variants for pre-aggregation

For roll-up tables, use AggregatingMergeTree or SummingMergeTree. They let you store pre-computed aggregates that amortize repeated work.

Step 4 — Ingestion strategies: from dual-write to CDC

There are two safe migration patterns for ingestion:

Dual-write + backfill: write to your existing analytics store and to ClickHouse. Backfill historical data with batch jobs. This is simplest for minimal disruption.
CDC to Kafka → ClickHouse: use Debezium or native CDC to publish changes to Kafka, then use ClickHouse's Kafka engine and materialized views for continuous ingestion. This minimizes duplicate writes and is production-ready at scale.

Example: Kafka materialized view pipeline

CREATE TABLE kafka_events ( ... ) ENGINE = Kafka SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic_list = 'events', kafka_format = 'JSONEachRow';

CREATE MATERIALIZED VIEW mv_events TO events AS
SELECT
  parseDateTimeBestEffort(event_time) AS event_time,
  tenant_id,
  user_id,
  event_type,
  properties,
  value
FROM kafka_events;

Use batch backfill for historical data with INSERT SELECT, and ensure idempotency where possible (use deduplication or ReplacingMergeTree if events can be reingested).

Step 5 — Query tuning and cost controls

Optimizing queries in ClickHouse is about reducing scanned bytes and making the engine use indexes and merge optimizations effectively.

Top tuning levers

ORDER BY and PARTITION choices (most important)
index_granularity: higher values reduce index size but may increase IO for selective filters
Compression codecs: ZSTD with appropriate level balances CPU vs storage
LowCardinality and Enum for repetitive strings
Use SAMPLE or approximate algorithms for non-critical dashboards (approx_count_distinct, reservoir sampling)
Materialized views for heavy JOIN/aggregation patterns

Practical query rewrites

Replace repeated subqueries with pre-aggregated tables.
Push predicates to WHERE so MergeTree can prune partitions.
Use arrayJoin sparingly—it's expensive if it inflates rows.

Step 6 — Storage & cost optimizations (tiering, TTL, downsampling)

Storage strategy is critical to long-term cost controls. ClickHouse supports tiered storage and TTLs to manage hot vs cold data.

Tiered storage best practices

Keep recent partitions on fast NVMe or local SSD for sub-second queries.
Move older partitions to cheaper S3-compatible object storage via storage policies.
Define TTL rules to DELETE or TO DISK or TO VOLUME older data

Example TTL:

ALTER TABLE events
MODIFY TTL event_time + INTERVAL 90 DAY TO VOLUME 'cold';

Downsample older data into hourly/day aggregates to preserve trends while reducing row counts. Implement a regular Merge job to compact aggregate tables.

Step 7 — Operations: monitoring, backups, and multi-tenancy

Operational maturity separates successful migrations from brittle ones. For hosts, shift from ad-hoc ops to automated, measurable processes.

Monitoring and observability

Export ClickHouse metrics with clickhouse_exporter to Prometheus.
Track system metrics: query_duration_ms, threads, merges, memory_usage, disk_usage, uncompressed_bytes_read.
Create dashboards for cost-sensitive KPIs: CPU-hours by tenant, bytes scanned per query, storage per tenant.

Backups and disaster recovery

Use S3-compatible snapshots or table-level backups. Test restores regularly.
For replicated clusters, monitor replication lag and broken parts.

Multi-tenancy and quotas

Enforce quotas at the user or tenant level (concurrency, memory, max_execution_time) and throttle heavy queries. For strict isolation, provide dedicated clusters for enterprise tenants.

Step 8 — Testing, validation, and migration runbook

Run through staged validation:

Unit test ingestion (schema match, nulls, encodings)
Functional test queries against sample data; compare results with source
Load test using replayed query traces to measure CPU & latency
Pilot with a small set of tenants for 2–4 weeks
Gradual cutover with monitoring and rollback plan

Rollback patterns: keep the old pipeline in read-only mode and fail traffic back if KPIs exceed thresholds for latency or cost. Make sure downstream consumers can handle schema differences during migration using feature flags or API versioning.

Case study (illustrative): 1-week pilot, 40% cost cut

Example pilot for a mid-sized hosting provider:

Scope: dashboard queries for 50 tenants (50M rows/day)
Action: dual-write + Kafka ingestion → ClickHouse MergeTree with monthly partitions and ORDER BY (tenant_id, event_time)
Optimization: materialized view for 5 heavy aggregations + LowCardinality for event_type
Results: median query latency down 6x; CPU-hours down 42% in production; storage per row down 3x after ZSTD compression; projected 40% monthly cost reduction for analytics tier

Numbers will vary, but this demonstrates the typical pattern: focused scope, schema changes, and a short pilot deliver measurable operational savings quickly.

Security, compliance, and governance

Hosting providers must treat analytics data with the same security posture as customer production data.

Enable TLS for client and replication traffic
Enforce RBAC: separate admin roles from tenant roles
Audit DDL and DML with logging into an append-only audit store
Encrypt data at rest when using shared object storage

KPIs and continuous optimization

Track these KPIs during and after migration:

CPU-hours per tenant per 1M queries
Average bytes scanned per query
Query error rate and p95 latency
Storage per month after compression and tiering

Set improvement targets (e.g., 30–60% CPU reduction for pilot workloads) and iterate on schema/queries for further gains.

Future trends and predictions (2026+)

Hosting providers should position for the next wave of analytics innovation:

Separation of compute and storage will become default, with ephemeral compute scaling to query demand and object stores holding cold partitions.
Managed, serverless OLAP options will mature—use them where you need rapid scale without ops overhead.
More advanced approximate algorithms and vectorized execution will further reduce CPU for non-exact analytics.
Better auto-tuning in operators and managed services will reduce manual index/granularity tuning.

Plan your architecture to be flexible: keep pre-aggregations and materialized views as part of the stack so you can move compute between self-hosted and managed providers without reworking queries.

Common migration pitfalls and how to avoid them

Trying to migrate everything at once: Start with a pilot on high-cost queries.
Ignoring schema design: Poor ORDER BY and partitions will negate ClickHouse benefits.
Underestimating ops: Monitoring and alerting are essential for safe cutover. See The Evolution of Site Reliability in 2026 for guidance.
Not enforcing quotas: Multi-tenant noise can kill performance—use query limits and resource groups.

Migration checklist (quick reference)

Identify high-cost queries and tenants
Prototype table design and ingestion (Kafka or batch)
Create materialized views for heavy aggregates
Implement tiered storage + TTL for old data
Instrument metrics and set alerts for CPU, bytes scanned, and p95 latency
Pilot with a small tenant cohort, then scale
Document rollback and restore procedures

Final recommendations — how to capture maximum ROI

For hosting providers, the most reliable path to savings is pragmatic and iterative: pick a high-impact workload, design for reads, use pre-aggregation, and automate tiering. Combine managed ClickHouse offerings for rapid onboarding with self-hosted clusters for high-volume tenants where you can squeeze cost further. Measure everything and set quarterly optimization sprints for the analytics fleet.

Call to action

If you want help building a migration roadmap tailored to your hosting environment, we can help: run a 2-week ClickHouse pilot with your top 5 costliest queries and deliver a validated cost model and migration runbook. Reach out to the beek.cloud team to schedule a free assessment and start lowering analytics query costs today.

beek

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.