ClickHouse for Scraped Data: Architecture and Best Practices

Hook: When scrapers drown your analytics pipeline — and how ClickHouse keeps the flow

If your scraping fleet produces millions of events per minute, you’re familiar with three constant headaches: keeping ingestion affordable, avoiding query lag for near‑real‑time dashboards, and pruning enormous raw logs without losing signal. In 2026 those problems are amplified by larger crawls, AI feature engineering, and stricter data governance. This guide gives pragmatic architecture patterns, OLAP schema recommendations, rollup strategies, and a side‑by‑side cost/latency lens vs Snowflake so you can choose the right tradeoffs for time‑series scraping logs.

Executive summary (most important first)

• ClickHouse excels for high‑throughput, low‑latency time‑series analytics when you need sustained ingestion and sub‑second query responses for dashboards and alerting.
• Design with a raw event table + rollup/aggregate layers (Materialized Views + Summing/CollapsingMergeTree) to minimize storage and speed queries.
• Use a streaming front‑end (Kafka/RabbitMQ) and ClickHouse engines (Kafka, Buffer, HTTP) to absorb bursts and enforce idempotency.
• For cost decisions: ClickHouse (self‑managed or ClickHouse Cloud) typically delivers better $/ingest and lower tail latency for time‑series workloads; Snowflake provides stronger operational simplicity and concurrency isolation but at higher continuous cost for heavy, streaming ingestion.
• Implement tiered retention: hot raw (30d), compacted rollups (1y), archive (Parquet on S3). Use TTLs and automated exports to enforce policy and control cost.

Why ClickHouse is a fit for scraped data in 2026

ClickHouse’s columnar engine and MergeTree family were built for analytical time‑series and high cardinality logs. Since late 2025 the ecosystem matured around managed cloud offerings, better Kafka integrations, and tooling for tiered storage — making ClickHouse a compelling option for high‑velocity scraping pipelines that need fast aggregations and long retention at controlled cost.

Key strengths for scraping workloads

• High sustained ingest throughput — optimized for multi‑GB/s inserts with background merges to minimize write stalls.
• Low query latency for point/time window aggregations and top‑N queries (useful for SLA dashboards and anomaly detection).
• Flexible TTLs and tiering — expire or move raw data automatically, and keep summaries for longer.
• Cost control via columnar compression, low‑cardinality encodings, and the option to self‑manage nodes for predictable bills.

Design patterns for ingestion pipelines

There are three common architectures for feeding scraped logs into ClickHouse. Choose based on your fleet size, reliability needs, and tolerance for operational work.

1) Stream-first (recommended for high throughput)

Pattern: Scrapers -> Kafka/RabbitMQ -> ClickHouse (Kafka engine or external consumer) -> Buffer/Merge

Why: Decouples producers from ClickHouse, allows replays, smooths spikes, and supports distributed scaling.

1. Produce normalized Avro/Protobuf messages containing job_id, url, url_hash, status, response_code, latency_ms, proxy_id, fingerprint, page_hash, snippets, scraped_at, raw_body_ref, and metadata.
2. Use the Kafka engine to stream into an intermediate ClickHouse table or deploy an external consumer (ksql, custom consumer) that writes to ClickHouse via HTTP batch inserts.
3. Insert into a Buffer table (small in‑memory buffer) to smooth microbursts and avoid backpressure on ClickHouse replicas.

2) Batch-first (for ETL/large replays)

Pattern: Scrapers -> compressed Parquet/NDJSON -> S3 -> ClickHouse (S3 table / external ETL)

Why: Cheap and robust for large replays, reprocessing, and cost‑efficient cold ingestion.

• Write periodic compressed Parquet files to S3. Use clickhouse-local or a small cluster job to bulk load into ClickHouse. Good for backfills, ML feature stores, or late‑arriving data. See a practical example in our field-tested runbooks for handling offline bulk loads and manifests.

3) Direct-write (for smaller fleets or integrations)

Pattern: Scrapers -> HTTPS Insert -> ClickHouse

Why: Simple. Use ClickHouse HTTP insert endpoint with JSONEachRow/CSV for low‑volume fleets or serverless scrapers.

Note: Add a buffering layer (Redis/Buffer table) if you expect bursts; see operational notes on avoiding backpressure in crawler architectures.

Schema recommendations: raw vs aggregated

Model scraped logs as an append‑only event stream with a small set of typed columns and avoid wide JSON blobs as first class query columns. Keep the raw body as a reference to object storage.

Raw event table (single wide table pattern)

Keep one canonical raw table for auditability and reprocessing. Example schema (MergeTree):

CREATE TABLE scraping.raw_events
(
  job_id String,
  request_id String,
  url String,
  url_hash UInt64,
  domain String,
  status UInt16,
  response_code UInt16,
  latency_ms UInt32,
  page_hash UInt64,
  proxy_id String,
  user_agent String,
  scraped_at DateTime64(3),
  observed_at DateTime64(3),
  raw_ref String, -- S3 key/URI
  tags Array(String),
  metadata String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(scraped_at)
ORDER BY (url_hash, scraped_at, request_id)
SETTINGS index_granularity = 8192;

Why these choices:

• Partition by month (toYYYYMM) reduces small part churn and keeps partition management predictable.
• ORDER BY (url_hash, scraped_at) collocates events for the same URL, which benefits range scans and dedup operations.
• Keep raw bodies out of the main table — store raw HTML as S3 references to avoid bloating compressed columns and keep hot queries lean. See practical S3 handling tips in this field guide on object storage and manifests.

Dimension and lookup tables

Create small dictionary tables for proxy metadata, job definitions, and canonical domains. Use ClickHouse Dictionary engine or periodic joins to avoid repeating large strings in the raw table.

Rollup / aggregated tables

Use Materialized Views to maintain hour/day summaries that feed dashboards and ML features. Examples:

CREATE MATERIALIZED VIEW scraping.hourly_agg TO scraping.hourly
AS SELECT
  toStartOfHour(scraped_at) AS hour,
  domain,
  count() AS requests,
  uniqExact(url_hash) AS unique_pages,
  quantile(0.95)(latency_ms) AS p95_latency
FROM scraping.raw_events
GROUP BY hour, domain;

CREATE TABLE scraping.hourly
(
  hour DateTime,
  domain String,
  requests UInt64,
  unique_pages UInt64,
  p95_latency UInt32
) ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(hour)
ORDER BY (domain, hour);

Use SummingMergeTree or ReplacingMergeTree depending on your rollup idempotency needs. For guidance on designing aggregates and monitoring their correctness, see notes on materialization and observability in cloud-native observability.

Rollup strategies and compaction

Rollups turn high‑cardinality raw events into compact metrics with huge storage savings. Use multiple rollup granularities and TTLs to balance detail and cost:

• Hot raw (detailed): keep full events for 7–30 days for debugging and reprocessing.
• Nearline hourly: aggregated hourly metrics for 3–12 months for most analytics.
• Long‑term daily: daily aggregates for trend analysis and ML training (1–3 years).
• Archive: export to Parquet on S3 for indefinite cold retention; see practical export patterns in a field-tested runbook.

Using TTL to automate tiering

ClickHouse has table TTLs to delete or move columns/rows after a date. Example: keep raw for 30 days then move to S3 table or delete.

ALTER TABLE scraping.raw_events
MODIFY TTL
  scraped_at + INTERVAL 30 DAY TO VOLUME 'cold';

In ClickHouse Cloud you can map volumes to different storage classes. In self‑managed clusters implement periodic export jobs to S3 for long‑term retention; see approaches to scheduling and export manifests in operational playbooks.

Idempotency, deduplication and schema evolution

Scraping pipelines face duplicate pages, retries, and schema drift. Use these patterns:

• Deterministic request_id or dedupe key (url_hash + job_id + attempt) and ReplacingMergeTree to keep the latest record for a key.
• CollapsingMergeTree to implement logical deletes or state changes (keep +1 / -1 markers for insert/delete semantics).
• Schema-on-write with small JSON metadata — store unstructured fields in a single JSON/Map column to reduce DDL churn and migrate structured fields when stable.

Performance tuning knobs that matter

Tune MergeTree and cluster settings to match ingest profiles:

• index_granularity: lower for fast point queries on high‑selectivity keys; higher for better compression.
• max_insert_block_size and insert_quorum: adjust for batch sizes and durability needs.
• merge_max_size and background pool sizes
• Use low_cardinality on high‑repetition strings (domains, statuses) to reduce memory footprint.

Operational patterns: scaling, replays, monitoring

Operational reliability matters as much as architecture:

• Backpressure & buffering: never rely on direct synchronous writes from scrapers at scale. Buffer in Kafka or local disk queue and let the streaming layer handle retries — a pattern explored in crawler operations guidance.
• Replays: store Kafka offsets or S3 file manifests so reprocessing is replayable and reproducible. Practical notes on manifests and offline reprocessing appear in our field-tested runbook.
• Monitoring: instrument insert latency, parts count, merge queue length, and query p95/p99. Alert on parts explosion (indicates many small inserts) or IO saturation — monitoring patterns overlap with edge and cloud observability practices described in edge observability and cloud-native observability.

Cost & latency comparison: ClickHouse vs Snowflake (practical view for 2026)

Both platforms are battle‑tested for analytics, but they serve different tradeoffs. Below is a practical comparison for time‑series scraping logs in 2026.

Ingestion cost and patterns

• ClickHouse: Lower $/GB for continuous streaming ingestion if self‑managed or using ClickHouse Cloud with a provisioned cluster. Columnar compression and rollups reduce storage. You pay for node VM hours and storage; efficient schema and rollups drive down long‑term cost.
• Snowflake: Very simple ingestion using Snowpipe and native COPY operations. Billing model (separate compute credits + storage) is convenient for bursty ad‑hoc loads. For sustained multi‑GB/s ingestion, Snowflake’s compute credit consumption can grow quickly because micro‑batches and auto‑scaling incur steady compute costs.

Query latency and concurrency

• ClickHouse: Sub‑second to low‑second analytics for typical time‑window queries and top‑N dashboards when data is properly partitioned and aggregates are precomputed.
• Snowflake: Excellent for ad‑hoc analytical SQL and complex joins; concurrency scales with virtual warehouses. For many small, frequent time‑window queries (dashboard refresh every 10s), cold warehouse spin‑up can add latency unless you run an always‑on warehouse.

Operational overhead

• ClickHouse: Self‑managed clusters require ops expertise (hardware, shards, replication). ClickHouse Cloud reduces that burden while preserving many cost/perf benefits.
• Snowflake: Minimal ops — the managed service handles scaling, vacuum, and storage tiering. Great if you prefer to trade unit cost for operational simplicity.

Realistic decision rule

Choose ClickHouse when you need sustained high ingestion throughput, sub‑second dashboards, and tight control over long‑term storage cost. Choose Snowflake for easy setup, heavy ad‑hoc SQL, and when you want fully managed scaling without cluster ops.

Example cost comparison (formulaic approach)

Avoid absolute numbers — costs vary. Use this quick formula to estimate which is cheaper for you:

1. Estimate raw daily ingest (GB/day) after compression (ClickHouse typically compresses logs 3–8x depending on schema).
2. Estimate compute cost: ClickHouse = node hourly cost * hours * node count. Snowflake = (warehouse size credits/hour * hours) * credit price.
3. Account for retention and rollups: how many GB remain in hot vs cold tiers.

When ingestion is continuous (24/7) and you keep raw logs hot for >30 days, ClickHouse often yields lower compute+storage costs per TB. If you only run occasional heavy queries and want zero ops, Snowflake’s convenience can offset higher unit costs.

2026 trends and the future

Recent momentum (late 2025 and early 2026) saw ClickHouse attract significant funding and expanded managed cloud features, improving onboarding and cross‑region replication. At the same time, the push to unlock tabular data for AI (see 2026 industry coverage) means teams will pipeline more structured scraped features into LLMs and tabular foundation models — increasing the demand for long‑lived, queryable time‑series stores. This favors architectures that keep compact, queryable aggregates alongside raw archives. For secure, latency‑sensitive edge workflows and regulatory controls see approaches in secure edge workflows.

Checklist: actionable takeaways to implement this week

• Define a canonical raw schema and move raw HTML to object storage with an S3 key column in ClickHouse.
• Deploy a streaming buffer (Kafka) between scrapers and ClickHouse to absorb retry storms and enable replays.
• Create hourly and daily materialized views for dashboard KPIs (requests, p95 latency, error rate, unique pages) using Summing/Grouping engines.
• Set TTLs: raw events (30 days), hourly aggregates (365 days), daily aggregates (3 years), and schedule Parquet exports for indefinite cold retention.
• Instrument metrics: insert latency, merge queue, parts count, and p99 query time. Automate alerts for parts explosion and IO saturation; reference cloud observability patterns in cloud-native observability.

Common pitfalls and how to avoid them

• Many tiny inserts: Batch inserts or use Buffer table to prevent a proliferation of small parts and high merge overhead. See operational notes and buffer patterns in edge backend guides.
• High cardinality string columns: Use low_cardinality, dictionary lookups, or create surrogate keys for domains and proxies.
• Keeping raw bodies hot forever: Move to S3 with a reference in ClickHouse to reduce cluster storage costs; follow export manifest patterns in the field runbook.
• Over‑reliance on JSON fields: Extract high‑value attributes into typed columns for faster aggregations and better compression.

Closing: how to choose and next steps

For teams running large scraping fleets in 2026 who need low‑latency dashboards, predictable ingest cost, and long retention with rollups, ClickHouse (self‑managed or ClickHouse Cloud) is typically the most efficient technical and cost fit. If you value operational simplicity and heavy ad‑hoc analytics over sustained ingest economy, Snowflake remains a strong alternative.

Ready to prototype? Start by ingesting a 24‑hour sample into a MergeTree raw table, create hourly materialized views, and measure p95 query latency and storage growth. Use the checklist above to validate cost and operational fit before committing to a production topology.

Call to action

Want a battle‑tested reference implementation (ClickHouse Docker compose + Kafka + example schemas and scripts) you can deploy in 30 minutes? Grab the repo and step‑by‑step runbook at the link below and spin up a 24‑hour ingest test with your sample crawler. Evaluate ingest throughput, query p95, and storage growth — then decide whether ClickHouse or Snowflake matches your operational and budget goals. Also see companion operational playbooks for turning experiments into repeatable replays in operational field reviews and marketplace-ready runbooks in hands-on runbooks.