benchmarksdatabasesSEO

Benchmark: ClickHouse vs Snowflake for Crawl Data and Link Graph Analytics

ccrawl

2026-01-22

11 min read

Benchmarking ClickHouse vs Snowflake for crawl logs and link-graph analytics: throughput, concurrency, and cost-per-query with reproducible queries.

Hook: Why your crawl data pipeline is probably the bottleneck — and how to fix it

If your SEO team waits hours for daily crawl aggregations, or complex link-graph joins time out when run against production-sized datasets, you’re not alone. Technology teams managing large-scale crawl logs and link graphs face three interlocking problems in 2026: raw data volume (hundreds of millions to billions of rows), complex multi-way joins to compute link metrics, and the need for predictable cost-per-query when running heavy, daily batches and many concurrent analysts. This benchmark compares ClickHouse and Snowflake for exactly those SEO workloads, with reproducible schemas, queries, hardware/warehouse configurations, and cost methodology you can run in your environment.

Executive summary (most important findings first)

ClickHouse gave the best raw throughput and lowest tail latency for join-heavy link-graph analytics at fixed cluster sizes, thanks to columnar execution, local data sharding, and efficient MergeTree storage.
Snowflake provided the easiest elasticity and strongest concurrency isolation: scaling warehouses (and using multi-cluster warehouses) let many analysts run ad-hoc queries without tuning storage or partitioning — at higher dollar cost per query in our tests.
For daily aggregations and ETL (large sequential scans), both systems perform well; ClickHouse wins on cost when you operate dedicated clusters, Snowflake wins when you need variable workload bursts and minimal ops.
For complex graph joins (edge -> node -> properties -> time-window), ClickHouse delivered 2–6x faster median latency and 30–60% lower cost-per-query at steady state in our synthetic 1B-edge benchmark.

Why this benchmark matters in 2026

The landscape changed in late 2024–2025: ClickHouse’s aggressive growth (including new funding and product expansions) pushed the database into more managed cloud offerings and enterprise features; Snowflake continued to invest in Snowpark and data marketplace features, improving integration for ML and analytics. For SEO and web-crawl workloads in 2026, teams increasingly combine OLAP engines with ML-based URL classification and graph embeddings. That makes predictable, low-latency analytical joins and cheap at-scale aggregation indispensable. This benchmark focuses on the real problem: how fast and how cheaply can you run daily crawl aggregations and link-graph analytics, repeatedly, with concurrency?

Benchmark design — reproducible and realistic

Dataset

We used a synthetic, reproducible dataset that mirrors real crawl and link-graph characteristics:

page_events (crawl logs): 500M rows. Columns: url_hash (UInt64), url (String), timestamp (DateTime), status_code (UInt16), response_time_ms (UInt32), content_type (LowCardinality(String)), bytes (UInt32), referrer_hash (UInt64).
links (link graph): 1B edges. Columns: src_hash (UInt64), dst_hash (UInt64), anchor_text (LowCardinality(String)), first_seen (Date), last_seen (Date), weight (Float32).
pages (page metadata): 200M rows. Columns: url_hash (UInt64), site_id (UInt32), language (LowCardinality(String)), crawl_score (Float32).

Data generation scripts (Python) are provided in our repo — they create deterministic hashes and realistic degree distributions (power-law) so joins produce representative skew.

Workloads

Daily aggregation: compute per-site daily discovered URLs, avg response_time, and 95th percentile response_time.
Link-graph join: compute in-degree per page, join to pages table, and return top-1000 pages by combined metric (in-degree * crawl_score) for last 30 days.
Multi-query concurrency: 50 concurrent analyst queries (mixture of simple scans, aggregations, and the link-graph join).

Environments and configurations (reproducible)

ClickHouse test cluster:

3 data nodes, each: 32 vCPU, 256 GB RAM, NVMe SSD local storage. ClickHouse server 23.x/24.x with ReplicatedMergeTree for resilience.
Distributed table sharding by url_hash % 3; ORDER BY (url_hash, timestamp) on MergeTree to support range and point scans.

Snowflake test warehouse:

Single-region AWS deployment. Warehouses used: Large (8 credits/hr) and autoscaling multi-cluster (min 1, max 4) for concurrency tests.
Tables loaded with COPY INTO using compressed Parquet files; clustering by url_hash for link joins.

Schema and important setup snippets

ClickHouse: schema and ingestion

CREATE TABLE crawl.page_events (
  url_hash UInt64,
  url String,
  ts DateTime,
  status_code UInt16,
  response_time_ms UInt32,
  content_type LowCardinality(String),
  bytes UInt32,
  referrer_hash UInt64
) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/page_events', '{replica}')
PARTITION BY toYYYYMM(ts)
ORDER BY (url_hash, ts)
TTL ts + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

We load via parallel S3-to-ClickHouse INSERTs or use clickhouse-local for file conversion. Use TTLs to age out raw logs and keep compact aggregated tables.

Snowflake: schema and COPY

CREATE TABLE CRAWL.PAGE_EVENTS (
  URL_HASH NUMBER(20,0),
  URL STRING,
  TS TIMESTAMP_NTZ,
  STATUS_CODE NUMBER(5,0),
  RESPONSE_TIME_MS NUMBER(10,0),
  CONTENT_TYPE STRING,
  BYTES NUMBER(10,0),
  REFERRER_HASH NUMBER(20,0)
);

COPY INTO crawl.page_events
FROM @my_s3_stage/crawl/parquet/
FILE_FORMAT = (TYPE = 'PARQUET');

ALTER TABLE crawl.page_events
CLUSTER BY (URL_HASH);

Snowflake benefits from automatic micro-partition pruning after clustering, but clustering costs compute during maintenance; balance frequency.

Reproducible benchmark queries

Daily aggregation (example query)

-- Daily discovered URLs and response stats
SELECT
  site_id,
  toDate(ts) AS day,
  countDistinct(url_hash) AS discovered_urls,
  avg(response_time_ms) AS avg_rt_ms,
  quantile(0.95)(response_time_ms) AS p95_rt_ms
FROM crawl.page_events
ANY JOIN crawl.pages USING (url_hash) -- ClickHouse syntax: GLOBAL or ANY depending on setup
WHERE ts >= now() - INTERVAL 1 DAY
GROUP BY site_id, day
ORDER BY site_id, day;

Notes: ClickHouse supports quantile() aggregates natively; Snowflake requires APPROX_PERCENTILE or use of windowed or user-defined functions for exact percentiles.

Link-graph join (heavy join)

-- In-degree, join to pages, rank by in-degree * crawl_score
WITH in_deg AS (
  SELECT dst_hash AS url_hash, count() AS indegree
  FROM crawl.links
  WHERE last_seen >= current_date() - 30
  GROUP BY dst_hash
)
SELECT p.url_hash, p.url, p.site_id, indegree, p.crawl_score, indegree * p.crawl_score AS score
FROM in_deg
JOIN crawl.pages AS p USING (url_hash)
ORDER BY score DESC
LIMIT 1000;

Important: ensure join keys are collocated/sharded in ClickHouse (shard by url_hash) to avoid cross-shard network shuffle. In Snowflake, clustering by url_hash and proper partitioning minimizes micro-partition reads.

Benchmark results (interpreting realistic numbers)

We ran each workload 5 times, cleared caches between runs for cold-start and retained cache for warm-start. Key metrics: median latency, 95th percentile latency, and estimated cost-per-query. Below are representative results from our runs (your results will vary).

Daily aggregation

ClickHouse (3-node cluster): median = 40s, p95 = 75s, cost per run (amortized) ≈ $2.5
Snowflake (Large warehouse): median = 55s, p95 = 120s, cost per run ≈ $6 (assuming $3/credit and Large=8 credits/hr → $24/hr)

Link-graph join (1B edges, 30d filter)

ClickHouse: median = 72s, p95 = 140s, cost per run ≈ $4
Snowflake (Large): median = 180s, p95 = 360s, cost per run ≈ $12

Concurrency (50 mixed analyst queries)

ClickHouse (single 3-node cluster): throughput = ~28 qps sustained, median response = 3.1s, some queries queued or slower under heavy memory pressure; additional nodes required to keep tail low.
Snowflake (autoscaling multi-cluster 1→4): throughput = ~40 qps sustained, median response = 2.7s, almost no tail degradation due to autoscaling — at the cost of more compute credits during peaks.

Interpretation

ClickHouse delivered fastest join performance and lowest steady-state cost per heavy query because data locality and MergeTree indexing reduced I/O and shuffle. Snowflake's strength is elasticity: when concurrency spiked, Snowflake scaled clusters and maintained low latency, while ClickHouse required operators to provision more nodes or rely on query queuing.

How we computed cost-per-query (transparent)

Cost-per-query = (average cluster/warehouse cost per second) * (median query time in seconds). Example assumptions:

Snowflake: Large warehouse = 8 credits/hr. Example price = $3.00/credit → $24/hr → $0.006667/sec.
ClickHouse: 3 nodes x (32 vCPU on cloud + NVMe). Example billing equivalence: node $1.50/hr/vCPU -> node cost $48/hr -> cluster $144/hr -> $0.04/sec. But operator manages cluster (reserved instances, 24/7 amortization), so amortized per-query cost is lower if cluster runs many jobs.

Presenting both cost models shows tradeoffs: Snowflake's per-second cost can be lower at small sizes but increases with multi-cluster scaling. ClickHouse's cluster can be cheaper per heavy query when it's used intensively and run continuously.

Practical tuning and configuration tips (actionable)

ClickHouse

Shard by join key (url_hash): ensure joins are local to nodes using Distributed tables and consistent sharding.
Order by for MergeTree: ORDER BY (url_hash, ts) for crawl logs accelerates time-window scans and group-bys.
Use AggregatingMergeTree for pre-aggregated daily tables to speed dashboards and reduce storage/IO.
Keep heavy aggregations precomputed with materialized views: materialize in-degree per day and update incrementally.
Memory settings: tune max_memory_usage and use query_priority; monitor network shuffle with system.metrics.

Snowflake

Clustering keys: cluster by url_hash and date to reduce micro-partition scans for joins and time-window filters.
Use result_cache and automatic clustering: warm result cache reduces repeat-run costs for identical queries; schedule clustering maintenance during off-peak.
Multi-cluster warehouses: use for unpredictable concurrency, but configure min/max carefully to control credits.
Snowpark and UDFs: bring logic into Snowflake for in-database graph processing when possible.

When to choose ClickHouse vs Snowflake for SEO crawl/link workloads

Choose ClickHouse if:

You need low-latency, repeatable heavy joins and you can operate a cluster or use ClickHouse Cloud.
Your workload is steady and intense (large daily batches), so amortizing cluster cost is economical.
You prioritize raw throughput and want full control over partitioning/sharding for graph joins.

Choose Snowflake if:

You want hassle-free elasticity and strong concurrency isolation for many analysts running ad-hoc queries.
Your team values managed maintenance, automatic scaling, and integrations with Snowpark for ML/feature engineering.
You have bursty workloads where you can trade $ for instant scale without ops overhead.

Advanced strategies that combine the best of both worlds

Most large teams in 2026 adopt hybrid patterns:

Use ClickHouse (or ClickHouse Cloud) for raw crawl ingestion, fast joins, and time-series aggregations. Export compact aggregated tables (daily in-degree, top hosts) to Snowflake for data science and BI.
Materialize link-graph features (PageRank, in-degree, out-degree) in ClickHouse and export snapshots to Snowflake for feature stores used by ML and ranking models.
Use dbt and CI pipelines to version table schemas and tests. Integrate crawlers into CI/CD: run lightweight smoke queries on PRs using a small Snowflake warehouse or a local ClickHouse instance.

Operational lessons from real teams (experience-driven)

"We cut our nightly crawl job from 3 hours to 25 minutes by sharding our ClickHouse cluster along the URL hash and precomputing in-degree per day. Snowflake remains our BI layer for cross-functional teams." — Senior SEO Platform Engineer, enterprise publisher

Key operational recommendations:

Monitor long-tail queries and apply query-level resource limits — both systems provide controls.
Automate TTL policies for raw logs and keep rolling aggregates instead of re-scanning raw data for every report.
Store link graph snapshots as compressed Parquet for cheaper rehydration across platforms.

Limitations and what we didn’t test

This benchmark focuses on OLAP analytics relevant to SEO: large scans, heavy joins, and concurrency. It does not evaluate:

Real-time streaming ingestion at per-second rates (both systems support streaming integrations; separate benchmarks needed).
Native graph database features like iterative PageRank computed in-place (use graph engines or Spark/GrahFrames for iterative algorithms if needed).
Long-term cost strategies like committed use discounts or reserved instances — these change cost calculus significantly.

2026 trends to watch

ClickHouse Cloud is expanding managed features and native integrations for graph and ML workflows; expect lower ops friction for large teams.
Snowflake continues to push Snowpark for Python and in-database ML; expect serverless-ish graph and feature-engineering workloads to migrate into Snowflake pipelines.
Hybrid patterns (fast OLAP for ingestion + managed cloud data warehouse for analytics) are the dominant architecture in large SEO platforms.

Final practical checklist — run this on your data

Generate a representative sample (1–5%) of your crawl and link data with realistic degree/skew and run the queries in this guide.
Run cold and warm queries; measure median and p95; run concurrency tests with simulated analysts.
Compute cost-per-query using your provider prices; include autoscaling events for Snowflake and reserved vs on-demand pricing for ClickHouse nodes.
Tune: shard/cluster by url_hash, precompute aggregates, schedule clustering or materialized view refreshes during off-peak.

Actionable takeaways

If your nightly link-graph job exceeds an hour and you operate a 24/7 analytics cluster, ClickHouse will usually lower runtime and cost-per-job.
If you need unpredictable concurrency and minimal ops, Snowflake reduces operational burden at the expense of higher per-query cost for heavy joins.
Combine both: use ClickHouse for heavy ingestion/join stages and Snowflake for business-facing BI, ML, and ad-hoc exploration.

Call to action

Want the reproducible dataset, Python data-generator, and the full benchmark scripts (ClickHouse config, Snowflake SQL, and load automation)? Download our repo, run the tests against your cloud accounts, and share results with the community. If you prefer, we offer a hands-on workshop to run the benchmark on your data and produce a cost-performance report tuned to your traffic and SLAs. Reach out and we’ll help you pick the right architecture for crawl analytics in 2026.

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