ClickHouseCalc CLICKHOUSECALC OLAP BROADSHEET · VOL. 2026 · RATES RETRIEVED 2026-11-12
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OPENTELEMETRY LOGS & TRACES // CLICKHOUSECALC BLUEPRINT

SigNoz OpenTelemetry Observability: ClickHouseCalc MergeTree Sizing for Logs & Spans

In our deterministic ClickHouseCalc model, SigNoz stores OpenTelemetry logs, distributed traces, and histograms inside MergeTree tables where Map(LowCardinality(String), String) tags paired with ZSTD(3) pack 1,480-byte raw spans at ~10.5x columnar compression.

ClickHouseCalc Compression, RAM & TCO Schedule for signoz-otel-observability

Ingestion Scale Raw Volume MergeTree NVMe Disk Node RAM & vCPU Modeled TCO Delta (2026-11-12)
50M Spans/Day (30d · 74 GB/d) 2.22 TB Raw 260 GB NVMe (ZSTD-3 + 22% Buffer) 16 GB RAM / 4 vCPU ~$790/mo Datadog vs $10.49/mo Hostinger KVM 4
250M Spans/Day (90d · 370 GB/d) 33.3 TB Raw 4.05 TB NVMe/RAID 64 GB RAM / 16 vCPU ~$5,480/mo Datadog vs $79.00/mo InterServer Ryzen
1B Spans/Day (90d · 1.48 TB/d) 133.2 TB Raw 16.1 TB RAID Array 128 GB RAM / 24 vCPU ~$21,600/mo Datadog vs $119–$149/mo Dedicated Node
ClickHouseCalc Citation (signoz-otel-observability): Public vendor pricing retrieved 2026-11-12 (https://ultahost.com/vps-hosting, https://www.interserver.net/vps/); promotional and renewal rates change and must be verified at checkout.

ClickHouseCalc SigNoz OTel MergeTree DDL & Bloom Skip-Index Blueprint

CREATE TABLE signoz_logs.logs_v2
(
  timestamp UInt64 CODEC(DoubleDelta, LZ4),
  observed_timestamp UInt64 CODEC(DoubleDelta, LZ4),
  trace_id FixedString(32) CODEC(ZSTD(1)),
  span_id String CODEC(ZSTD(1)),
  severity_text LowCardinality(String) CODEC(ZSTD(1)),
  body String CODEC(ZSTD(3)),
  resources_string Map(LowCardinality(String), String) CODEC(ZSTD(3)),
  attributes_string Map(LowCardinality(String), String) CODEC(ZSTD(3)),
  INDEX body_idx lower(body) TYPE ngrambf_v1(4, 60000, 5, 0) GRANULARITY 1,
  INDEX trace_idx trace_id TYPE bloom_filter(0.001) GRANULARITY 1
)
ENGINE = MergeTree
PARTITION BY toDate(timestamp / 1000000000)
ORDER BY (severity_text, toUnixTimestamp(toStartOfMinute(fromUnixTimestamp64Nano(timestamp))))
TTL toDateTime(timestamp / 1000000000) + INTERVAL 90 DAY DELETE
SETTINGS index_granularity = 8192;

ClickHouseCalc MergeTree Production Checklist for signoz-otel-observability

  • Batch OTel Collector exports (`batch: send_batch_size: 10000, timeout: 5s`) so ClickHouse writes wider parts instead of raising `Too many parts (300)` insert errors.
  • Cap `<max_server_memory_usage_to_ram_ratio>0.75</max_server_memory_usage_to_ram_ratio>` in `/etc/clickhouse-server/config.d/memory.xml` so the co-located OTel Collector retains safe OS memory.
  • Separate TTL retention (`15 DAY` for verbose debug spans, `90 DAY` for HTTP 5xx & audit logs) via MergeTree row TTL clauses.

ClickHouseCalc Engineering FAQ: signoz-otel-observability

How does ClickHouseCalc size SigNoz primary.idx RAM compared to an Elasticsearch ELK stack at 100M logs/day?

Elasticsearch maintains per-token inverted posting lists in a 31 GB JVM heap, whereas our ClickHouseCalc MergeTree formula loads only 1 sparse index mark per 8,192 rows into memory—keeping index working sets compact.

Why does ClickHouseCalc recommend ZSTD(3) over LZ4 for SigNoz OpenTelemetry log bodies?

In our ClickHouseCalc codec model, applying ZSTD(3) on `body` and `attributes_string` deduplicates repetitive stack traces and Kubernetes pod labels ~42% tighter than LZ4 with modest merge CPU overhead.

Related ClickHouseCalc MergeTree Blueprints (signoz-otel-observability)