ClickHouse Analytics Distributed System —

ClickHouse Distributed Analytics

ClickHouse Columnar OLAP Distributed Shard Replica SQL Petabyte Analytics Log Time Series Dashboard Real-time Production

Feature	ClickHouse	PostgreSQL	Elasticsearch
Use Case	OLAP Analytics	OLTP + Analytics	Search + Log
Storage	Columnar	Row-based	Inverted Index
Speed (Analytical)	เร็วมาก 100x+	ช้า (Full Scan)	เร็ว (Aggregation)
Compression	10-40x	2-3x	1.5-3x
SQL Support	Full SQL	Full SQL	Limited (DSL)
Scale	Petabyte	Terabyte	Petabyte
UPDATE/DELETE	Async (ช้า)	Fast (MVCC)	Fast

Cluster Architecture

# === ClickHouse Distributed Cluster ===

# Cluster Config (config.xml)
# 
#   
#     
#       
#         
#           clickhouse-01
#           9000
#         
#         
#           clickhouse-02
#           9000
#         
#       
#       
#         
#           clickhouse-03
#           9000
#         
#         
#           clickhouse-04
#           9000
#         
#       
#     
#   
# 

# Create Replicated Table
# CREATE TABLE events ON CLUSTER analytics_cluster (
#     date Date,
#     user_id UInt64,
#     event_type LowCardinality(String),
#     properties String,
#     timestamp DateTime
# ) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/events', '{replica}')
# PARTITION BY toYYYYMM(date)
# ORDER BY (date, user_id, event_type);

# Create Distributed Table
# CREATE TABLE events_distributed ON CLUSTER analytics_cluster
# AS events
# ENGINE = Distributed(analytics_cluster, default, events, rand());

from dataclasses import dataclass

@dataclass
class ClusterNode:
    shard: int
    replica: str
    host: str
    role: str
    data_size: str

nodes = [
    ClusterNode(1, "A", "clickhouse-01", "Primary", "~500GB"),
    ClusterNode(1, "B", "clickhouse-02", "Replica", "~500GB"),
    ClusterNode(2, "A", "clickhouse-03", "Primary", "~500GB"),
    ClusterNode(2, "B", "clickhouse-04", "Replica", "~500GB"),
    ClusterNode(3, "A", "clickhouse-05", "Primary", "~500GB"),
    ClusterNode(3, "B", "clickhouse-06", "Replica", "~500GB"),
]

print("=== Cluster Architecture (3 Shards x 2 Replicas) ===")
print(f"Total Data: ~1.5TB (500GB/shard x 3)")
print(f"Total Nodes: 6 (HA: ทุก Shard มี Replica)\n")
for n in nodes:
    print(f"  Shard {n.shard} Replica {n.replica}: {n.host} ({n.role}) {n.data_size}")

Query Optimization

# === Query Optimization Techniques ===

@dataclass
class OptTechnique:
    technique: str
    sql_example: str
    speedup: str
    when_to_use: str

techniques = [
    OptTechnique("Primary Key (ORDER BY)",
        "ORDER BY (date, user_id, event_type)",
        "10-100x (skip irrelevant granules)",
        "เลือก Column ที่ Filter WHERE บ่อยที่สุด"),
    OptTechnique("Partitioning",
        "PARTITION BY toYYYYMM(date)",
        "10-50x (skip irrelevant partitions)",
        "Time-based Data ที่ Query ตาม Date Range"),
    OptTechnique("Materialized View",
        "CREATE MATERIALIZED VIEW daily_stats ENGINE=SummingMergeTree "
        "AS SELECT date, count() FROM events GROUP BY date",
        "100-1000x (pre-aggregated)",
        "Dashboard Query ที่ Aggregate ซ้ำบ่อย"),
    OptTechnique("LowCardinality",
        "event_type LowCardinality(String)",
        "2-5x (dictionary encoding)",
        "Column ที่มี Distinct Values < 10,000"),
    OptTechnique("Projection",
        "ALTER TABLE events ADD PROJECTION p1 "
        "(SELECT * ORDER BY event_type, date)",
        "5-50x (pre-sorted copy)",
        "Query ที่ Filter ไม่ตรงกับ Primary Key"),
    OptTechnique("Skip Index (bloom_filter)",
        "INDEX idx_user bloom_filter(user_id) GRANULARITY 4",
        "2-10x (skip granules)",
        "Column ที่มี High Cardinality เช่น user_id"),
]

print("=== Optimization Techniques ===")
for t in techniques:
    print(f"\n  [{t.technique}] Speedup: {t.speedup}")
    print(f"    SQL: {t.sql_example}")
    print(f"    When: {t.when_to_use}")

Monitoring & Operations

# === Production Monitoring ===

# Prometheus Exporter
# docker run -d --name ch-exporter \
#   -e CLICKHOUSE_SERVER=clickhouse-01:8123 \
#   -p 9116:9116 \
#   f1yegor/clickhouse-exporter

# Key System Tables
# SELECT * FROM system.query_log WHERE type='QueryFinish' ORDER BY query_duration_ms DESC LIMIT 10;
# SELECT * FROM system.metrics;
# SELECT * FROM system.replicas WHERE is_leader=1;

@dataclass
class MonitorMetric:
    metric: str
    source: str
    threshold: str
    action: str

metrics = [
    MonitorMetric("Query Duration P99",
        "system.query_log",
        "> 30 seconds",
        "Optimize Query Primary Key Materialized View"),
    MonitorMetric("Memory Usage",
        "system.metrics (CurrentMetric_MemoryTracking)",
        "> 80% RAM",
        "ลด max_memory_usage ต่อ Query Scale Up RAM"),
    MonitorMetric("Replication Lag",
        "system.replicas (absolute_delay)",
        "> 10 seconds",
        "ตรวจ Network ZooKeeper HealthCheck"),
    MonitorMetric("Disk Usage",
        "system.disks",
        "> 80%",
        "TTL ลบข้อมูลเก่า Move ไป Cold Storage"),
    MonitorMetric("Merge Queue",
        "system.metrics (BackgroundMergesInProgress)",
        "> 20 pending merges",
        "เพิ่ม background_pool_size หรือ Scale"),
    MonitorMetric("Insert Rate",
        "system.events (InsertedRows)",
        "Drop > 50% จาก Normal",
        "ตรวจ Source Pipeline ClickHouse Health"),
]

print("=== Production Metrics ===")
for m in metrics:
    print(f"  [{m.metric}] Source: {m.source}")
    print(f"    Threshold: {m.threshold}")
    print(f"    Action: {m.action}")

เคล็ดลับ

• ORDER BY: เลือก Primary Key ที่ตรงกับ WHERE บ่อยที่สุด
• MV: ใช้ Materialized View สำหรับ Dashboard Query เร็ว 100x+
• LowCardinality: ใช้ LowCardinality สำหรับ Low Distinct Column
• TTL: ตั้ง TTL ลบข้อมูลเก่าอัตโนมัติ ประหยัด Storage
• Keeper: ใช้ ClickHouse Keeper แทน ZooKeeper (เร็วกว่า ง่ายกว่า)

ClickHouse คืออะไร

Open Source Columnar OLAP Yandex Vectorized SIMD Compression 10-40x SQL Petabyte Log Analytics Time Series Dashboard Real-time