How to Use Columnar OLAP (ClickHouse) to Speed Up Exploratory Analysis of Scraped Datasets

Speed up discovery: why ClickHouse matters for large scraped corpora in 2026

Scrapers now produce terabytes of semi-structured content daily. Analysts and data engineers are blocked by slow exploratory queries, high cloud costs, and brittle pipelines that fall over when sites or schemas change. ClickHouse, the high-performance columnar OLAP engine, has become a go-to platform for interactive exploration of large scraped datasets because it compresses massively, executes aggregations orders of magnitude faster than row stores, and supports real-time streams at scale. With the project and ecosystem expanding rapidly after ClickHouse's late12025 fundraise, 2026 is an excellent moment to adopt columnar OLAP for scraping pipelines.

What this tutorial covers (practical, tactical, and 2026-aware)

• A resilient ingestion architecture for scraped data (batch + streaming) using ClickHouse best practices.
• Schema design patterns for exploratory analysis: raw landing tables, distilled analytics tables, and materialized views.
• Query patterns and tricks to speed iteration: sampling, approximate aggregates, data skipping indexes, and projections.
• Performance & cost optimizations: codecs, TTL hot/warm storage, partitioning, and engine choices.
• Concrete SQL examples you can copy and adapt.

Quick architecture: from scraper to ClickHouse

Keep the ingestion pipeline simple and fault-tolerant. Use the following layers:

1. Scraper fleet 1 workers produce JSON/CSV or write raw HTML to object storage (S3-compatible) and emit messages to Kafka or a streaming bus.
2. Raw landing table 1 ClickHouse stores raw payloads (compressed), enabling re-parsing and ad-hoc extraction without re-scraping.
3. Parsed / analytical tables 1 materialized views or ETL jobs normalize key fields and compute derived metrics for faster querying.
4. Exploration layer 1 analysts run interactive queries on distilled tables and use sampling / approximate queries when appropriate.

Designing the raw landing table: keep everything needed to reprocess

The raw table is your insurance policy. Store the original payload, a fetch-time timestamp, response code, fetch metadata, and a stable identifier (e.g., SHA256 of the canonical URL). Use compression to minimize storage cost.

Example: raw_scrapes table (MergeTree)

CREATE TABLE raw_scrapes (
  url String,
  url_hash UInt64, -- e.g., cityHash64 of canonical URL
  fetched_at DateTime64(3),
  status_code UInt16,
  content_type String,
  raw_body String CODEC(ZSTD(3)),
  meta JSON DEFAULT '{}', -- optional structured metadata
  source String -- crawler id
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(fetched_at)
ORDER BY (url_hash, fetched_at)
SAMPLE BY url_hash;
  

Why this shape?

• Partitioning by month makes TTL and cold-store movement easy.
• ORDER BY url_hash groups versions of the same page together, enabling ReplacingMergeTree or dedup logic later.
• SAMPLE BY lets analysts use the SAMPLE clause to run fast exploratory queries over representative subsets.

Streaming ingestion: Kafka engine + materialized views

For near-real-time scraping, push messages to Kafka and use ClickHouse's Kafka engine + Materialized View to populate the raw table and downstream summarized tables. This pattern minimizes ETL jobs and keeps ClickHouse as the central ingestion point.

Example: Kafka 1 raw table

CREATE TABLE kafka_raw (
  msg String
) ENGINE = Kafka('kafka:9092', 'scrapes', 'group1', 'JSONEachRow');

CREATE MATERIALIZED VIEW mv_kafka_to_raw TO raw_scrapes AS
SELECT
  JSONExtractString(msg, 'url') AS url,
  cityHash64(JSONExtractString(msg,'url')) AS url_hash,
  parseDateTimeBestEffort(JSONExtractString(msg,'fetched_at')) AS fetched_at,
  toUInt16(JSONExtractString(msg,'status')) AS status_code,
  JSONExtractString(msg,'content_type') AS content_type,
  JSONExtractString(msg,'body') AS raw_body,
  JSONExtractRaw(msg, 'meta') AS meta,
  JSONExtractString(msg,'source') AS source
FROM kafka_raw;
  

This lets scrapers write once to Kafka; ClickHouse pulls, parses, and persists. When you design this for edge and multi-region deployments, consult guides on edge migrations and sharding best practices.

From raw to analytical: use materialized views to distill fields

Raw payloads are great for reprocessing but slow to analyze. Create derived tables optimized for queries analysts run most: domain, title, word counts, language, detected schemas, price/sku extractions, and signals like robots status.

Example distilled table + materialized view

CREATE TABLE docs_analytics (
  url_hash UInt64,
  url String,
  fetched_at DateTime64(3),
  domain LowCardinality(String),
  title String,
  language LowCardinality(String),
  word_count UInt32,
  price Float64,
  sku LowCardinality(String)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(fetched_at)
ORDER BY (domain, fetched_at);

CREATE MATERIALIZED VIEW mv_extract_docs TO docs_analytics AS
SELECT
  url_hash,
  url,
  fetched_at,
  extractDomain(url) AS domain,
  HTMLExtractText(raw_body, 'title') AS title,
  detectLanguage(raw_body) AS language,
  length(splitByChar(' ', HTMLExtractText(raw_body, 'body'))) AS word_count,
  extractPrice(raw_body) AS price,
  extractSKU(raw_body) AS sku
FROM raw_scrapes;
  

Note: replace helper functions (extractDomain, HTMLExtractText, detectLanguage, extractPrice) with your actual UDFs or SQL parsing using ClickHouse functions or external processors.

Schema design guidelines for exploratory analysis

• Keep a narrow, query-optimized analytics table with most-used fields as separate columns and low-cardinality encodings where possible.
• Use LowCardinality(String) for fields with many repeated values (domains, language, status, etc.). It reduces memory and speeds GROUP BY.
• Denormalize for analytics 1 wide tables often beat joins for exploration.
• Store raw payloads separately to avoid scanning large blobs for routine queries.
• Set ORDER BY to support common range scans 1 e.g., ORDER BY (domain, fetched_at) if analysts usually query by domain over time.

Query patterns that make interactive exploration fast

When your dataset is billions of rows, run queries that avoid full-table scans during exploration. These patterns are tested in production scraping environments:

1) Sampling to iterate fast

SELECT domain, count() AS cnt
FROM docs_analytics
SAMPLE 0.01
GROUP BY domain
ORDER BY cnt DESC
LIMIT 50;
  

Sampling is ideal for discovering signals. Ramp to full queries once you know the right filters.

2) Approximate aggregations

SELECT domain, uniqCombined(url_hash) AS unique_pages
FROM docs_analytics
WHERE fetched_at >= now() - INTERVAL 7 DAY
GROUP BY domain
ORDER BY unique_pages DESC
LIMIT 50;
  

Use uniqCombined (or other approximate functions) to get fast, memory-efficient counts. For small result sets, run exact computations when needed.

3) Data skipping with indices

Create data skipping indexes for high-cardinality tokens (e.g., product SKUs or tokens in raw HTML). These indexes dramatically reduce I/O for selective queries.

ALTER TABLE raw_scrapes
ADD INDEX idx_url_hash url_hash TYPE minmax GRANULARITY 3;

ALTER TABLE docs_analytics
ADD INDEX idx_sku sku TYPE set(200) GRANULARITY 4;
  

4) Pre-aggregations and projections

Materialized views that maintain rollup tables (daily aggregations per domain) let analysts query precomputed results instantly. ClickHouse projections (a lightweight pre-aggregation inside a table) are especially useful for repeated query patterns.

5) Use stateful aggregates for complex pipelines

Aggregate functions that return states (e.g., uniqState) can be merged later 1 perfect for distributed ingestion where intermediate states are transmitted instead of raw rows.

Tuning performance and cost in 2026: practical knobs

ClickHouse gives many levers. Focus on these that yield the best cost/perf tradeoffs for scraped datasets.

Compression codecs per column

Text-heavy columns (raw_body) benefit from strong compression (ZSTD), while numeric columns can use LZ4 for faster CPU decompress. Example column codec:

raw_body String CODEC(ZSTD(6)),
word_count UInt32 CODEC(LZ4)
  

TTL + storage policies for hot/warm/cold

Move older scrapes to cheaper storage automatically:

ALTER TABLE raw_scrapes
MODIFY TTL fetched_at + INTERVAL 90 DAY TO VOLUME 'cold';
  

Use a separate cluster volume or S3-backed volume for cold data to cut storage costs while keeping raw payloads for compliance or retrospective reprocessing. See our notes on storage considerations for on-device AI and personalization when designing retention and privacy controls.

Engine choice: ReplacingMergeTree & AggregatingMergeTree

For deduplication, use ReplacingMergeTree with a version column. For rollups, AggregatingMergeTree stores aggregate states for fast merges.

CREATE TABLE deduped_scrapes (
  url_hash UInt64,
  fetched_at DateTime64(3),
  raw_body String,
  version UInt64
) ENGINE = ReplacingMergeTree(version)
PARTITION BY toYYYYMM(fetched_at)
ORDER BY (url_hash);
  

Data skipping indices and bloom filters

Use minmax indices for range queries and bloom_filter or set indices for selective token lookups. They dramatically reduce IO for selective analytics queries.

Cluster topology & the Distributed engine

For massive corpora, shard by url_hash and use replicas for HA. Create a Distributed table that routes queries to local shards and only aggregates results. Use local tables for heavy ETL to avoid cross-node traffic during ingest. If you9re moving to multi-region or edge deployments, review edge migration strategies and consider how shard keys interact with latency requirements.

Operational best practices

• Backfill and schema evolution: keep the raw table as a replay source. If you add a new column to analytics, re-run a materialized view creation job from raw payloads rather than changing upstream scrapers. For migrations and provider changes, the Email Exodus guide has useful patterns for migrating stateful systems.
• Monitoring: track part counts, compaction pressure, and query latency. MergeTree part explosion is a common cause of slowdowns after many small inserts. Good observational practices are close cousins of evidence capture patterns described in edge evidence capture.
• Batching: prefer micro-batches (100010,000 rows) to single-row inserts to reduce part churn.
• Cost tracking: monitor storage volumes and cold transitions; move historical raw payloads to object storage with pointers stored in ClickHouse if cold access is rare.

Real-world example: diagnosing content drift across 100M pages

Scenario: You need to identify domains where average page word count has dropped by >30% month-over-month across 100M pages. With properly designed analytics tables, the query becomes interactive:

WITH
  period1 AS (
    SELECT domain, avg(word_count) AS avg1
    FROM docs_analytics
    WHERE fetched_at BETWEEN toDateTime('2025-11-01') AND toDateTime('2025-11-30')
    GROUP BY domain
  ),
  period2 AS (
    SELECT domain, avg(word_count) AS avg2
    FROM docs_analytics
    WHERE fetched_at BETWEEN toDateTime('2025-12-01') AND toDateTime('2025-12-31')
    GROUP BY domain
  )
SELECT p1.domain, avg1, avg2, (avg2/avg1) AS ratio
FROM period1 p1
JOIN period2 p2 USING domain
WHERE ratio < 0.7
ORDER BY ratio
LIMIT 200;
  

With pre-aggregations and LowCardinality domains, this runs fast even at 100M+ rows. If too slow initially, run the calculation on a SAMPLE then scale up. For heavy compute and AI-augmented analysis (embedding + ANN), consider the hardware implications discussed in RISC-V + NVLink conversations: throughput and accelerator topology matter when pairing OLAP with vector workloads.

2026 trends to leverage when building your pipeline

• Growing managed ClickHouse Cloud offerings 1 they simplify cluster management and provide integrated storage policies (hot/warm/cold) that reduce ops overhead; pair these with local-first designs when you need offline or low-latency capabilities such as those described in local-first edge tools.
• Integration of OLAP and vector search 1 embedding stores and approximate nearest neighbor libs are increasingly paired with ClickHouse for hybrid analytics + semantic search workflows; review LLM file-security tradeoffs when designing where embeddings and raw payloads live.
• Streaming-first ingestion patterns 1 Kafka + ClickHouse materialized views became a de facto standard in late 2025 for low-latency scraping analytics.
• Observation: major vendors and startups are investing in ClickHouse tools for data governance and SQL UDFs, improving compliance and reprocessing capabilities. If you need to audit governance or cut hidden costs, see the playbook on auditing legal/tech stacks.

Common pitfalls and how to avoid them

• Ingesting raw blobs into analytic tables. Fix: keep raw in a landing table and push parsed fields into analytics tables.
• Too many small inserts (part explosion). Fix: batch inserts, tune the flush interval, and consider buffer tables.
• Wrong ORDER BY keys. Fix: pick keys that match query patterns 1 the wrong key forces many granules to be scanned.
• Over-indexing. Fix: use a few well-placed skipping indexes and monitor maintenance overhead.

Actionable checklist to get started (30/60/90 day plan)

1. 30 days: Stand up a ClickHouse test cluster or use a managed ClickHouse Cloud trial. Create raw landing and analytics tables as above. Ingest a representative sample (15M rows). If you9re running in constrained network environments, validate performance on edge network hardware.
2. 60 days: Implement Kafka ingestion, materialized views, and a couple of pre-aggregations. Add sampling and approximate queries to analyst dashboards. Set up TTL policies for raw data lifecycle.
3. 90 days: Shard and scale to production size. Add data skipping indices based on query logs and enable hot/warm storage policies to optimize cost. Run cost/perf benchmarks and tighten retention or move cold raw payloads to object storage.

Practical rule: design for fast iteration first 1 use sampling and approximate functions to find signals. Optimize for full-scale runs only after you know the right filters.

Final takeaways

• ClickHouse is purpose-built for the kind of heavy aggregations and interactive queries analysts need when exploring scraped corpora.
• Separate raw storage from analytics 1 treat raw payloads as replayable material, and keep analytics tables narrow and denormalized.
• Use streaming ingestion (Kafka + materialized views) to simplify pipelines and reduce ETL code.
• Exploit sampling, approximate aggregates, and skipping indices to keep queries interactive and cost-efficient.
• Leverage 2026 ecosystem improvements 1 managed ClickHouse services, integrated storage policies, and vector-search integrations make mixed OLAP+semantic workflows practical.

Next steps 1 try this on your data

Start with a 15M row sample of your scraped corpus and implement the raw 1 analytics pattern shown above. Measure query latency for a set of analyst queries before and after adding sampling, skipping indices, and pre-aggregations. Iterate: tune ORDER BY keys and partitioning based on the observed query shapes. If you need help hardening CI/CD and patch workflows around your ingestion and query fleet, see our integration notes on automating virtual patching and secure release strategies.

If you'd like hands-on help adapting these patterns to your scraping pipeline, we can benchmark your corpus, propose a shard & storage plan, and deliver a ClickHouse schema and ingestion playbook tailored to your use case. Reach out to test a production-proof architecture and lower your per-query costs while improving analyst velocity.

Call to action

Ready to make exploratory analysis of scraped datasets fast and repeatable? Spin up a ClickHouse trial, ingest a sample dataset, and use the SQL patterns above. When you want to scale, contact our team for a tailored ClickHouse ingestion and cost-optimization plan.

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