Hybrid Storage Architectures for Time‑Series at Scale: Balancing PLC Flash, SSD, and Object Storage

Hook: When telemetry costs and latency collide — you need a hybrid storage architecture

If your organization struggles with growing telemetry volumes, ballooning SSD costs, and queries that either run fast or cheap — but never both — you're not alone. Developers and platform engineers in 2026 face an inflection point: higher-density flash (PLC/QLC) is becoming viable at scale while OLAP engines such as ClickHouse continue to dominate sub-second analytics. The pragmatic answer is a hybrid storage architecture that combines cheap PLC/edge SSDs for hot writes, mid-tier SSDs for warm queries, and object storage for long-lived cold retention.

The 2026 context: why hybrid matters now

Two trends converged into an operational opportunity late 2025 and early 2026:

• Flash innovation: vendors (notably SK Hynix) made progress on multi‑level cell techniques (PLC and other denser cells) that push GB-per-dollar downwards while improving endurance tradeoffs for telemetry workloads.
• OLAP momentum: ClickHouse’s rapid growth and investment in 2025–2026 (major funding and product expansion) means teams can build low‑latency analytics around tiered storage APIs such as S3 without rebuilding storage engines from scratch.

That combination lets you design architectures tuned for the real constraints of telemetry: very high ingest rates, high cardinality, long retention windows, and unpredictable query patterns.

Core principles for hybrid time-series storage

1. Tier for cost and performance — hot (low latency), warm (fast scans), cold (cheap long-term).
2. Use the right medium for each life stage — PLC/cheap SSD at edge gateways or hot nodes; higher-end NVMe for warm indexes; object storage for cold archives.
3. Push compute to where metadata needs fast access — keep indexes and recent parts local in an OLAP engine, offload immutable blobs to object stores.
4. Automate lifecycles — TTLs, partition moves, and downsampling should operate without manual intervention.
5. Measure cost per GB and read/write economics — object storage has lower capacity cost but different read/egress costs that affect query placement.

Prescriptive architecture patterns

Below are three practical patterns with real-world tradeoffs and step-by-step components you can adapt.

1) Edge-buffered ingestion with central ClickHouse cluster

Use case: industrial telemetry or distributed sensors with intermittent connectivity and high burstiness.

• Edge gateways have PLC-optimized or low-cost SSDs for local buffering and short-term queries (hours–days).
• Gateways batch and stream data to a central Kafka/Kinesis topic. Central ingestion nodes write to ClickHouse using native insertion or a Kafka engine.
• Central ClickHouse nodes use a hot volume on local SSD (NVMe or high-end PLC) for recent parts, and a cold volume pointed at object storage for older parts.

Why this works: buffering at the edge reduces tail latency for ingestion and prevents traffic spikes from overwhelming the cluster. The central ClickHouse cluster exposes global analytics while offloading capacity to object storage.

2) Hybrid OLAP with warm tier SSDs for query paths

Use case: high‑cardinality telemetry where analysts need fast historical queries (days/weeks) but not necessarily millisecond access to months-old data.

• Architect ClickHouse with three tiers: hot (local NVMe), warm (cheap enterprise SSDs), cold (S3/object).
• Keep primary MergeTree indexes and recent parts on hot; move compressed immutable parts older than X days to warm volume where they still serve queries fast; push older parts to object storage.
• Leverage ClickHouse TTL and storage policies to automate migration: TTL to DISK/VOLUME, MOVE PARTITION, and remote reads when required.

This reduces total cost while keeping typical query latencies in the low seconds rather than tens of seconds when scanning cold archive files.

3) Object-first cold store with materialized aggregation for long retention

Use case: long‑term retention (years) for regulatory compliance or model training where full resolution is rarely needed.

• Ingest raw telemetry into ClickHouse/OLAP for 7–30 days at full resolution.
• Create periodic materialized views that downsample data (minute/hour aggregates) and store these in a warm/warm‑cold tier for long-term queries — this pattern pairs well with on-device visualization and summarization strategies described in on-device data visualization work.
• Export raw parts to object storage as compressed Parquet (or ClickHouse native parts in S3) for archival; leave rich indexes only in warm tier to facilitate occasional rehydration.

Advantages: low storage cost for cold data, fast access to aggregated summaries, and the ability to rehydrate high‑resolution slices when required.

ClickHouse-specific implementation — configuration and SQL recipes

ClickHouse supports hybrid storage via the storage_policy configuration and TTL rules. Below are practical snippets you can adapt.

Storage configuration (simplified)

<yandex>
  <storage_configuration>
    <disks>
      <hot>
        <path>/var/lib/clickhouse/disk/hot/</path>
      </hot>
      <warm>
        <path>/var/lib/clickhouse/disk/warm/</path>
      </warm>
      <cold>
        <type>s3</type>
        <endpoint>https://s3.amazonaws.com</endpoint>
        <access_key_id>YOUR_KEY</access_key_id>
        <secret_access_key>YOUR_SECRET</secret_access_key>
        <bucket>your-clickhouse-archive</bucket>
        <path>clickhouse-parts/</path>
      </cold>
    </disks>

    <policies>
      <hybrid_policy>
        <volumes>
          <volume>
            <disk>hot</disk>
          </volume>
          <volume>
            <disk>warm</disk>
          </volume>
          <volume>
            <disk>cold</disk>
          </volume>
        </volumes>
      </hybrid_policy>
    </policies>
  </storage_configuration>
</yandex>

Table design and TTLs

Design MergeTree with a partition key that suits your retention policy (daily/hourly). Use TTL to move parts automatically:

CREATE TABLE telemetry_raw (
  device_id String,
  ts DateTime64(3),
  metrics Nested(name String, value Float64),
  tags Map(String, String)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (device_id, ts)
SETTINGS storage_policy = 'hybrid_policy';

ALTER TABLE telemetry_raw
  MODIFY TTL ts + INTERVAL 7 DAY TO VOLUME 'warm',
            ts + INTERVAL 90 DAY TO VOLUME 'cold';

In this example: recent 7 days remain on hot; 7–90 days move to warm; >90 days go to cold (object storage).

Downsampling and aggregates

CREATE MATERIALIZED VIEW mv_telemetry_hourly
TO telemetry_hourly
AS
SELECT
  device_id,
  toStartOfHour(ts) AS hour,
  argMax(metrics.name, ts) AS last_metric_names,
  avg(metrics.value) AS avg_value,
  max(metrics.value) AS max_value
FROM telemetry_raw
GROUP BY device_id, hour;

Store telemetry_hourly on the warm tier for quick historical queries while the raw remains in cold.

Data movement & lifecycle operations

Operational commands you will use regularly:

• ALTER TABLE ... MOVE PARTITION ... TO VOLUME — explicit partition move when rebalancing or repairing.
• OPTIMIZE TABLE ... FINAL — controls compaction behavior so parts are consolidated before moving.
• TTL expressions with TO DISK/TO VOLUME — automated lifecycle rules enforced by ClickHouse.

Cost modeling: math you can use

To make an informed decision, build a capacity-cost model. Here’s a concise formula:

TCO_month = (size_hot * cost_hot) + (size_warm * cost_warm) + (size_cold * cost_cold) + egress + ops

Example (illustrative ranges as of 2026):

• hot NVMe SSD price (provisioned): $0.05–0.20 / GB-month (varies by provider & endurance)
• warm cheaper SSDs / QLC: $0.02–0.08 / GB-month
• object storage (standard): $0.02–0.03 / GB-month; archive tiers: $0.003–0.01 / GB-month

Keep in mind object tiers have request and egress costs — include estimated monthly read-GB for queries that will hit cold storage. For telemetry-heavy systems, warm tier sizes often dominate cost savings because they balance query performance against capacity price.

Hardware placement: where to put PLC/cheap SSDs

PLC or denser QLC SSDs are most valuable at:

• Edge gateways — cost-efficient burst buffers and local short-term analytics.
• ClickHouse hot nodes that absorb write peaks but only hold a sliding window of recent parts.
• Warm-tier nodes where throughput is steady and rebuilds from object storage are infrequent.

Do not place PLC-only as the sole persistent layer for your entire cluster if you require high write amplification durability — mix PLC with better endurance drives for metadata-heavy operations. If you operate field kits or kiosk-style ingest points, review real-world portable power and field gear recommendations so remote nodes remain resilient.

Operational best practices and pitfalls

Backups and disaster recovery

Even with object storage, ensure you backup ClickHouse metadata and restore processes. Object storage reduces recovery time for bulk data but not for cluster-specific state (Zookeeper/ClickHouse Keeper metadata).

Cold-read performance

Expect higher latency for reads that trigger object-store retrievals. Mitigations:

• Prefetch common partitions to warm tier using scheduled jobs.
• Maintain precomputed aggregates for long-range queries.
• Use object-storage with low-latency GET (multi-region edge caches) when available.

Compression and codecs

Choose compressions that trade CPU vs size. ZSTD with medium level (e.g., level 3–5) often hits a practical sweet spot for telemetry. For archived Parquet, columnar compression (Snappy, ZSTD) depends on your downstream ML/analytics needs. If you run a set of small, distributed capture devices, check techniques described for on-device summarization to reduce archive volume before export.

Monitoring and observability

Track:

• Disk occupancy per volume
• Number of parts and compaction lag
• Object-store egress bytes and request counts
• Rehydration frequency — indicates whether migration policies need tuning

Also consider your wider tooling footprint — too many niche tools increase ops load; see guidance on tool sprawl and consolidation.

Case study: real-world pattern (anonymized)

Company X ingests 20TB/day of sensor telemetry. They implemented:

• Edge buffers on PLC-enabled gateways storing 24 hours of data locally (reduces 99th percentile ingestion latency).
• Central ClickHouse cluster with hot=7d, warm=90d, cold=archive (>90d) using S3.
• Materialized hourly aggregates stored on warm tier and weekly aggregates pushed to an analytics lake in Parquet for ML.

Result: 60% reduction in storage TCO vs a purely SSD solution and median query latency for 30‑day windows under 2s for typical queries, while archival restores for full-resolution investigations took minutes-to-hours depending on scope.

Future-proofing: trends to watch in 2026 and beyond

• PLC and 5-bit-per-cell devices will continue to mature — expect better endurance and lowered GB costs across edge and server SSDs.
• ClickHouse and similar OLAP vendors will add more robust cloud-native tiering tools and S3-backed MergeTree improvements, further simplifying hybrid deployments.
• Object storage vendors are introducing faster retrieval classes and compute-near-storage options; these reduce cold-query penalties. Track the evolution of edge-powered approaches and data fabrics to keep architectural decisions current.

Operational takeaway: design for motion not stasis — your architecture must expect parts to migrate between tiers automatically, and your observability stack must validate those movements in production.

Checklist: getting started in 90 days

1. Measure current telemetry growth and query patterns by retention window (0–7d, 7–30d, 30–90d, >90d).
2. Set clear SLAs for latency and cost per retention band.
3. Deploy a small ClickHouse cluster with a hybrid storage_policy and simulate TTL movements on a subset of data.
4. Design materialized views for downsampling and test typical analyst queries against warm tier only.
5. Choose object-storage lifecycle rules (Standard, Infrequent, Archive) and incorporate request/egress costs in models.
6. Run a 30-day pilot with PLC/cheap SSDs at the edge or on hot nodes and measure durability, throughput, and rebuild time. For remote kiosks and roadcase deployments, consult hardware guidance such as resilient roadcase lighting and field kit advice.

Actionable takeaways

• Start with measurement: Base tier boundaries on real query and retention patterns, not guesses.
• Automate lifecycle: Use ClickHouse TTLs and storage policies to ensure predictable costs and minimal operator toil.
• Mix drive classes: Combine PLC/cheap SSD for short windows and standard NVMe for metadata-heavy workloads.
• Downsample eagerly: Materialized aggregates save query cost and reduce cold reads.
• Model economics: Include egress and request pricing when evaluating cold vs warm placement.

Conclusion & next steps

In 2026, hybrid storage architectures are no longer an academic exercise — they are the practical lever teams use to balance sub-second queries against multi-year retention at an acceptable cost. PLC and denser flash types make hot/warm tiers more affordable; object storage provides a near-infinite cold sink. The right combination, automated via ClickHouse storage policies, TTLs, and downsampling, gives you a predictable TCO and fast analytics for the lifecycle of your telemetry.

Call to action

Ready to prototype a hybrid architecture for your telemetry? Start with a cost/latency snapshot of your current workload and run a 30‑day ClickHouse pilot using the storage_policy pattern above. If you want, download our 90‑day runbook and example ClickHouse configs to accelerate implementation — contact our team to get the playbook and a short architecture review tailored to your telemetry profile.

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