ClickHouseCalc CLICKHOUSECALC OLAP BROADSHEET · VOL. 2026 · RATES RETRIEVED 2026-11-12
VI · Bản Tiếng Việt

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REAL-TIME ROLLUPS & TICKS // CLICKHOUSECALC BLUEPRINT

ClickHouse Incremental Materialized Views: ClickHouseCalc `AggregatingMergeTree` Sizing

ClickHouseCalc Editorial Desk · Public Vendor Rates Retrieved 2026-11-12

In our deterministic ClickHouseCalc model, ClickHouse Materialized Views operate as insert-time stream triggers—transforming incoming tick batches into compact `AggregatingMergeTree` state blocks before raw rows even merge on disk.

Verify UltaHost Specs for materialized-views-realtime-bi via ClickHouseCalc → Compare InterServer Options for materialized-views-realtime-bi via ClickHouseCalc → Load Workload in ClickHouseCalc MergeTree Calculator →

ClickHouseCalc Compression, RAM & TCO Schedule for materialized-views-realtime-bi

Ingestion Scale Raw Volume MergeTree NVMe Disk Node RAM & vCPU Modeled TCO Delta (2026-11-12)
100M Ticks/Day + 4 MVs (365d · 38 GB/d) 13.87 TB Raw Ticks 1.35 TB Gen4 NVMe (LZ4 + DoubleDelta) 32 GB RAM / 8 vCPU ~$1,950/mo Cloud Warehouse vs $37.50–$58/mo UltaHost VDS
1B Ticks/Day + 8 MVs (365d · 380 GB/d) 138.7 TB Raw Ticks 10.6 TB NVMe (ZSTD-3 + 22% Buffer) 64–128 GB RAM / 24 vCPU ~$11,400/mo Cloud Warehouse vs $109.00/mo InterServer Ryzen
ClickHouseCalc Citation (materialized-views-realtime-bi): 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 1-Minute OHLCV `AggregatingMergeTree` & Trigger View SQL

CREATE TABLE market.ohlcv_1m_target
(
  symbol LowCardinality(String),
  bucket DateTime CODEC(DoubleDelta, LZ4),
  open_price AggregateFunction(argMin, Float64, DateTime64(9)),
  high_price SimpleAggregateFunction(max, Float64),
  low_price SimpleAggregateFunction(min, Float64),
  close_price AggregateFunction(argMax, Float64, DateTime64(9)),
  volume SimpleAggregateFunction(sum, Float64)
)
ENGINE = AggregatingMergeTree
PARTITION BY toYYYYMM(bucket)
ORDER BY (symbol, bucket);

CREATE MATERIALIZED VIEW market.ohlcv_1m_mv TO market.ohlcv_1m_target AS
SELECT
  symbol,
  toStartOfMinute(exchange_ts) AS bucket,
  argMinState(price, exchange_ts) AS open_price,
  max(price) AS high_price,
  min(price) AS low_price,
  argMaxState(price, exchange_ts) AS close_price,
  sum(qty) AS volume
FROM market.raw_ticks
GROUP BY symbol, bucket;

ClickHouseCalc MergeTree Production Checklist for materialized-views-realtime-bi

  • Batch source inserts (`>= 10,000` rows per block) because every insert block into `raw_ticks` synchronously emits a corresponding aggregation part into each attached Materialized View.
  • Enable `parallel_view_processing = 1` on 8+ vCPU servers so chained Materialized Views execute across cores in parallel.
  • Pair a shorter 14-to-30-day TTL on raw nanosecond ticks with multi-year retention on the compact `ohlcv_1m_target` rollup table.

ClickHouseCalc Engineering FAQ: materialized-views-realtime-bi

Why does ClickHouseCalc require `-Merge` combinators when querying `AggregatingMergeTree` rollup tables?

Because background part compactions run asynchronously, multiple unmerged parts may hold partial states for the same `(symbol, bucket)` key; wrapping queries with `GROUP BY symbol, bucket` and `argMinMerge()` finalizes exact results at read time.

When does ClickHouseCalc favor `LZ4` over `ZSTD(3)` on hot financial tick ingestion partitions?

For ultra-hot 7-day partitions ingesting monotonic nanosecond timestamps and prices, `DoubleDelta` already strips numeric redundancy while `LZ4` minimizes insert CPU overhead.

Related ClickHouseCalc MergeTree Blueprints (materialized-views-realtime-bi)