ClickHouse vs Elasticsearch 2026: Log Analytics and Search Comparison

ClickHouse and Elasticsearch both handle log analytics and observability data, but with different architectural approaches. Elasticsearch excels at full-text search and unstructured data, while ClickHouse delivers superior performance for structured analytical queries. As log volumes grow and costs become critical, understanding these trade-offs helps you choose the right platform.

Platform Overview

ClickHouse

ClickHouse is a columnar analytical database optimised for fast aggregations and analytical queries on structured data.

Strengths for log analytics:

• Exceptional query performance on large datasets
• Superior compression (10-20x)
• Cost-effective storage
• Real-time ingestion
• SQL query language

Elasticsearch

Elasticsearch is a distributed search and analytics engine built on Apache Lucene, optimised for full-text search and unstructured data.

Strengths for log analytics:

• Powerful full-text search
• Flexible schema (schema-on-read)
• Rich ecosystem (ELK stack)
• Kibana visualisation
• Mature APM integration

Architecture Comparison

ClickHouse Log Architecture

┌─────────────────────────────────────────┐
│            Log Pipeline                 │
│  ┌─────────┐    ┌─────────────────────┐ │
│  │ Vector  │───▶│     ClickHouse      │ │
│  │ Fluent  │    │  ┌───────────────┐  │ │
│  │  Bit    │    │  │  MergeTree    │  │ │
│  └─────────┘    │  │  (Columnar)   │  │ │
│                 │  └───────────────┘  │ │
│                 └─────────────────────┘ │
└─────────────────────────────────────────┘
         │
         ▼
    Grafana / Custom Dashboards

Elasticsearch Log Architecture

┌─────────────────────────────────────────┐
│            ELK Stack                    │
│  ┌─────────┐    ┌─────────────────────┐ │
│  │Logstash │───▶│   Elasticsearch     │ │
│  │ Beats   │    │  ┌───────────────┐  │ │
│  │         │    │  │   Inverted    │  │ │
│  └─────────┘    │  │    Index      │  │ │
│                 │  └───────────────┘  │ │
│                 └──────────┬──────────┘ │
│                            │            │
│                 ┌──────────▼──────────┐ │
│                 │       Kibana        │ │
│                 └─────────────────────┘ │
└─────────────────────────────────────────┘

Performance Comparison

Query Performance

Query Type	ClickHouse	Elasticsearch
Count logs (1B rows)	0.5s	3-5s
Aggregation by field	0.8s	5-10s
Time-series grouping	0.3s	2-4s
Full-text search	2-5s	0.5-1s
Regex search	1-3s	3-8s
Complex aggregations	1-2s	10-30s

ClickHouse advantages:

• 5-10x faster for aggregations
• Better performance on structured queries
• Efficient time-series operations
• Lower resource consumption

Elasticsearch advantages:

• Superior full-text search
• Better fuzzy matching
• Relevance scoring
• More flexible text analysis

Ingestion Performance

Metric	ClickHouse	Elasticsearch
Ingestion rate	1M+ events/sec	100K-500K events/sec
Indexing overhead	Minimal	Significant
Write latency	<100ms	1-5 seconds (refresh)
Resource efficiency	High	Moderate

Storage and Compression

Storage Comparison

Data Volume	ClickHouse	Elasticsearch
Raw logs: 1TB	50-100GB	300-500GB
30-day retention	~3TB stored	~15TB stored
Compression ratio	10-20x	2-3x

ClickHouse’s columnar storage and compression algorithms achieve significantly better storage efficiency:

-- ClickHouse: Check compression ratio
SELECT
    table,
    formatReadableSize(sum(bytes_on_disk)) AS compressed,
    formatReadableSize(sum(data_uncompressed_bytes)) AS uncompressed,
    round(sum(data_uncompressed_bytes) / sum(bytes_on_disk), 2) AS ratio
FROM system.parts
WHERE active
GROUP BY table

Cost Comparison

Storage Costs (100TB raw logs/month)

Component	ClickHouse	Elasticsearch
Stored volume	~8TB	~40TB
Storage cost	~$200/month	~$1,000/month
Compute (equivalent)	$2,000/month	$5,000/month
Total estimate	~$2,500/month	~$8,000/month

ClickHouse typically costs 60-70% less than Elasticsearch for log analytics workloads. For cost optimisation strategies, see our cloud cost management guide.

Feature Comparison

Feature	ClickHouse	Elasticsearch
Full-text search	Basic	Excellent
Structured queries	Excellent	Good
Aggregations	Excellent	Good
Compression	10-20x	2-3x
Schema	Required	Flexible
Query language	SQL	Query DSL / SQL
Visualisation	Grafana	Kibana
APM integration	Via OTEL	Native
Alerting	External	Built-in
Machine learning	Limited	X-Pack ML

Use Case Recommendations

Choose ClickHouse When:

High-volume log analytics

-- Fast aggregations on billions of logs
SELECT
    toStartOfHour(timestamp) AS hour,
    level,
    service,
    count() AS log_count,
    countIf(level = 'ERROR') AS errors
FROM logs
WHERE timestamp >= now() - INTERVAL 24 HOUR
GROUP BY hour, level, service
ORDER BY hour DESC, errors DESC

Cost-sensitive observability

• High log volumes where storage costs matter
• Long retention requirements
• Metrics and structured event data

Real-time dashboards

• Sub-second query requirements
• High-concurrency dashboard serving
• Time-series visualisation with Grafana

Choose Elasticsearch When:

Full-text log search

{
  "query": {
    "bool": {
      "must": [
        { "match": { "message": "connection timeout" } },
        { "range": { "@timestamp": { "gte": "now-1h" } } }
      ],
      "filter": [
        { "term": { "service": "api-gateway" } }
      ]
    }
  }
}

APM and distributed tracing

• Elastic APM integration
• Full ELK stack benefits
• Kibana dashboards and alerting

Unstructured data exploration

• Unknown log formats
• Text-heavy analysis
• Fuzzy and proximity searches

Migration Strategies

Elasticsearch to ClickHouse

Many organisations migrate to ClickHouse for cost savings:

Step 1: Schema design

CREATE TABLE logs (
    timestamp DateTime64(3),
    level LowCardinality(String),
    service LowCardinality(String),
    host LowCardinality(String),
    message String,
    trace_id String,
    span_id String,
    attributes Map(String, String)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (service, timestamp)
TTL timestamp + INTERVAL 30 DAY

Step 2: Ingestion pipeline

• Use Vector, Fluent Bit, or Logstash
• Configure output to ClickHouse
• Maintain parallel ingestion during migration

Step 3: Query migration

-- Elasticsearch Query DSL equivalent in ClickHouse SQL
SELECT *
FROM logs
WHERE message ILIKE '%connection timeout%'
  AND timestamp >= now() - INTERVAL 1 HOUR
  AND service = 'api-gateway'
ORDER BY timestamp DESC
LIMIT 100

Hybrid Architecture

Run both for different use cases:

┌─────────────┐
│   Logs      │
└──────┬──────┘
       │
       ▼
┌──────────────┐
│   Vector/    │
│   Kafka      │
└──────┬───────┘
       │
   ┌───┴───┐
   ▼       ▼
┌──────┐ ┌──────────────┐
│Click │ │Elasticsearch │
│House │ │              │
└──┬───┘ └──────┬───────┘
   │            │
   ▼            ▼
Grafana     Kibana
(Analytics) (Search)

Observability Stack Integration

ClickHouse in Observability

ClickHouse integrates with modern observability stacks:

• Metrics: Prometheus remote write, VictoriaMetrics
• Logs: Vector, Fluent Bit, Logstash
• Traces: Jaeger with ClickHouse backend
• Visualisation: Grafana with native plugin

For comprehensive observability guidance, see our observability platforms comparison.

Elasticsearch in Observability

Elasticsearch powers the Elastic Observability suite:

• Elastic APM: Full application performance monitoring
• Filebeat/Metricbeat: Log and metric collection
• Kibana: Unified visualisation and alerting
• Machine learning: Anomaly detection

Operational Considerations

ClickHouse Operations

Advantages:

• Simpler cluster management
• Lower resource requirements
• Predictable performance
• Easy horizontal scaling

Challenges:

• Schema management required
• Less mature ecosystem
• Fewer managed options

Elasticsearch Operations

Advantages:

• Mature operational tooling
• Many managed service options
• Rich ecosystem
• Built-in monitoring

Challenges:

• JVM tuning complexity
• Higher resource requirements
• Shard management overhead
• Index lifecycle management

Conclusion

ClickHouse and Elasticsearch serve different primary purposes in log analytics:

Choose ClickHouse for high-volume structured log analytics where query performance and cost efficiency are priorities. It excels at aggregations, time-series analysis, and scenarios where SQL queries on structured data dominate.

Choose Elasticsearch when full-text search capabilities are essential, you need the complete ELK ecosystem, or APM integration with Elastic Observability is required.

Consider both in a hybrid architecture where ClickHouse handles structured analytics and Elasticsearch provides full-text search capabilities.

For help building your observability infrastructure, contact our team to discuss your log analytics requirements.

Related Resources

• How Tasrie IT Services Uses ClickHouse
• Top 10 Observability Platforms
• ClickHouse vs TimescaleDB 2026
• Cloud Native Database Guide 2026

External Resources:

• ClickHouse Documentation
• Elasticsearch Documentation
• Vector Log Collector
• ClickHouse for Observability