Cheap Archival + Fast Hot Storage: Build a Commodity Price Archiver on PLC SSDs

Cheap Archival + Fast Hot Storage: Build a Commodity Price Archiver on PLC SSDs

Hook: If unpredictable cloud bills and endless ops work are eating your time, you can archive high-frequency commodity ticks at a fraction of the cost by combining PLC-backed cold nodes with a tiny, high-performance hot cache. This guide shows how to design, deploy and automate a production-ready system in 2026 that minimizes maintenance while keeping queries fast for analytics and realtime dashboards.

Why PLC SSDs Matter in 2026

By late 2025 the industry accelerated adoption of PLC (penta-level cell) flash for bulk, read-mostly workloads. Manufacturers improved cell management and error mitigation (some vendors used novel cell-splitting and firmware techniques) to make PLC viable for archival SSDs. The net result for architects in 2026:

• Much lower $/GB for dense NVMe SSDs compared with earlier-generation QLC parts.
• Good random-read performance for analytic queries when files are well-organized and compressed.
• Tradeoffs: lower write endurance and higher raw error rates than TLC — which you manage with erasure coding, careful write patterns and lifecycle management.

Design goal: Put cold, compressed, time-series segments on PLC-backed nodes for cost-efficient retention, and keep a small hot cache on low-latency TLC/QLC NVMe or in-memory indexes for recent-window queries.

High-level architecture

Components

• Ingest layer: containers or serverless functions that accept tick streams and write compressed blocks.
• Hot cache: small stateful service (Redis+local RocksDB or TimescaleDB / ClickHouse hot partitions) holding recent minutes/hours of data for sub-second queries.
• Cold archive nodes (PLC): clustered storage nodes with PLC NVMe drives hosting compressed partitioned files (Parquet/Arrow) or an S3-compatible object store layer.
• Orchestration & automation: Kubernetes + CSI drivers for NVMe, Terraform for infra, ArgoCD/GitHub Actions for CI/CD.
• Query/analytics: Pre-aggregations, time-range pruning and vectorized engines (ClickHouse/DuckDB) to keep scans minimal.

Flow

1. Tick arrives → ingestion service writes to hot cache and appends to local rolling segment (e.g., 5–15s files).
2. Background process compacts/encodes segments into columnar files (Parquet with Zstandard or Arrow IPC files) and pushes them to PLC-backed nodes.
3. Querying layer checks hot cache for recent data, and reads columnar segments from cold nodes for historical ranges; pre-aggregates reduce I/O.

Data model and sizing example (practical)

Start with a sizing exercise so you can dimension hot cache and cold nodes correctly.

Assumptions

• 100 commodity symbols
• Average 10 ticks/sec per symbol → 1,000 ticks/sec total
• 86,400 seconds/day → ~86.4M ticks/day
• Binary compact record: timestamp (8B), price (8B), size (4B), flags/index (2B) ≈ 22B raw
• Delta-encoding + Zstd compression typically yields 3–8x reduction for contiguous time-series; use conservative 4x

Daily storage

Raw: 86.4M * 22B ≈ 1.9 GB/day. After compression (4x) ≈ 0.48 GB/day. Add metadata, secondary indexes and redundancy — budget 1 GB/day. That’s ~30 GB/month, ~360 GB/year per 100-symbol set.

If you run 1,000 symbols or higher volatility (more ticks), scale linearly. These numbers show why PLC-backed nodes become compelling: even at high-frequency, raw volumes are manageable with right encoding and retention policies.

Storage format & encoding: what to use

When the objective is low cost on PLC with decent read latency for analytic queries, pick a columnar format optimized for reads and compression.

• Parquet + Zstd (level 3–5): great for batch analytics, works with ClickHouse/DuckDB/Presto.
• Arrow IPC / Feather: faster in-memory handoff for low-latency analytics when using vectorized engines.
• Segmentation strategy: time-based partitions (per-minute or per-5-minute files) and symbol-based grouping to prune reads.
• Delta encoding: store timestamp deltas and price deltas before compression to multiply compression gains.

Hot cache sizing & architecture

The hot cache must handle real-time dashboards and frequent sub-second queries. Keep it intentionally small to limit cost and ops.

• Primary store: Redis (streams + sorted sets) or an in-process LSM (RocksDB) for per-symbol latest-window storage (e.g., last 5–60 minutes).
• Secondary analytical cache: a hot partition of ClickHouse/TimescaleDB storing recent hours for OLAP queries.
• Size rule of thumb: Cache the last 5–60 minutes in memory: for 1,000 ticks/sec and 60 minutes, 3.6M ticks; at 22B raw = 75MB raw, ~20MB compressed — trivial. But account for indexes, overhead; budget 1–2GB for comfort.

Cold nodes: hardware and software specifics

PLC drives require different operational guardrails than TLC. Design for read-mostly, append and repair workflows.

Hardware

• Enterprise PLC NVMe drives (U.3 / EDSFF where available) in server nodes with 2–8TB capacities.
• Prefer hardware with built-in power-loss protection and vendor firmware that supports advanced ECC.
• Network: 25–100GbE for cluster nodes to support parallel reads.

Software

• Use an S3-compatible object store layer (MinIO/OpenIO/SeaFile) on top of PLC drives if you want object semantics and lifecycle policies.
• Alternatively, use file-based layout on ZFS or Ceph with erasure coding to minimize replication overhead (cheaper than triple replication).
• Prefer erasure codes tuned for recovery cost (e.g., 10+2) rather than 3-way replication to save space.

Endurance & write strategy

Because PLC has lower write endurance, design to:

• Append segments and compact rarely; avoid frequent small writes.
• Write-once segments: write rolling files locally (on cheaper temp storage or RAM disk), then flush compressed segments to PLC nodes in larger bulk.
• Throttle background compaction to avoid simultaneous heavy writes across all drives.

Query performance patterns

To keep queries fast against cold PLC-backed storage:

• Partitioning: by symbol and date-range so queries only read necessary files.
• Indexes: Build a compact per-file summary (min/max timestamp, min/max price, sampling histogram) stored with the file for quick pruning.
• Pre-aggregations: Maintain minute/hour aggregates in a cheap store (SSTables or a time-series DB) to reduce reads for charts.
• Vectorized reads: Use ClickHouse or DuckDB for ad-hoc analytics; they pull only needed columns and use SIMD to accelerate scans.

Practical rule: optimize for scanning compressed columnar segments. Good partitioning and per-file summaries are worth their weight in CPU — they save expensive I/O from cold drives.

Automation & CI/CD – example pipeline

Automate everything you can — provisioning, volume attachment, deployment, compaction jobs and lifecycle policies.

Infrastructure as code

• Use Terraform for node pools, BGP/LoadBalancer config and any on-prem networking.
• Provision NVMe volumes and attach via a CSI plugin or specialized NVMe-over-Fabrics (NVMe-oF) initiator if you disaggregate storage.

Application delivery

• Containerize ingest and compaction services. Use multi-stage builds to keep images small.
• ArgoCD (GitOps) or Flux to apply Helm charts for services: ingress, hot cache, compactor, archive-store.
• GitHub Actions for CI: run unit tests for encoders, build artifacts, and push images to a registry.

Operational jobs

• Scheduled Kubernetes CronJobs or a serverless job queue for compaction jobs that create Parquet segments.
• Use Prometheus + Grafana for monitoring: IOPS, average read latency, error-corrected blocks, SMART stats for PLC drives, compaction job duration.
• Automated repairs: when SMART or SMART-like telemetry indicates rising uncorrectable errors, schedule data rebuilds from parity copies.

Ingestion options: containers vs serverless

Both approaches are valid — choose based on burstiness and cost profile.

• Serverless (Cloud Run / AWS Lambda): good for bursty public feed ingestion; write to hot cache (managed Redis) and append to a message queue (Kafka / Pulsar / Kinesis) for downstream compaction.
• Containers (Kubernetes): better for sustained high-throughput ingestion (1k+ TPS) and for using local NVMe for rolling segments.

Security & compliance

• Encrypt-at-rest: use LUKS/Tang or cloud disk encryption + KMS for key management.
• Encrypt-in-transit: mTLS for node-to-node and TLS for client APIs.
• Access control: IAM roles for ingestion components, and signed URLs or token-based access for archived files.
• Audit logging: track who queried what time ranges (important for commercial feed licensing).

Cost model & example

Always produce a cost model from assumptions. Example (conservative, illustrative):

• Storage: 1TB PLC server node amortized cost (hardware+power+space+ops) ≈ $40–80/month. (Assumption: PLC reduces $/GB vs TLC by ~30–50% in 2026 data-center SKUs.)
• Hot cache: small VM + Redis cluster ≈ $50–200/month depending on HA needs.
• Networking & IOPS: budget extra for cross-node reads during compaction and analytical queries — maybe $20–100/month at moderate scale.

So for a modest deployment storing several TB of compressed tick data you might be in the low hundreds per month instead of thousands on premium NVMe or cloud GA storage classes. The exact numbers depend on your provider and region — always benchmark.

Operational best practices

• Run regular recovery drills: restore a day's worth of segments from parity and verify checksums.
• Monitor PLC-specific telemetry: error correction rate, retention-level warnings, and throughput anomalies.
• Maintain retention tiers: recent fast queries in hot cache, nearline month on cheaper PLC nodes, and long-term compressed snapshots in object cold vault (or tape / cloud glacier equivalents if needed).
• Use canary deployments for compaction algorithm changes — a buggy compactor can corrupt multiple files rapidly.

Example deployment pattern (Kubernetes + CSI + MinIO)

One practical pattern is:

1. Provision nodes with PLC NVMe drives and install an NVMe CSI driver that exposes the drives as block devices to pods.
2. Run MinIO in distributed mode with erasure coding across PLC nodes (object interface simplifies lifecycle and multi-tenant access).
3. Ingest containers write to hot cache and push compacted Parquet to MinIO buckets.
4. ArgoCD manages application manifests and CronJobs for compaction and retention tasks.

2026 trends & future-proofing

As of 2026, expect these trends to shape future designs:

• PLC enters mainstream: vendors will standardize firmware and telemetry making PLC more predictable for bulk storage use cases.
• Zoned/host-managed SSDs (ZNS) and NVMe-oF: better for large, append-only workloads; plan for zone-aware clients to reduce write amplification.
• Edge-to-cloud pipelines: More hybrid workflows will push initial compaction to edge gateways before shipping compressed segments to PLC clusters.
• Compute-storage disaggregation: Expect storage nodes to serve many compute clusters via RDMA/NVMe-oF — design your file layout and partitioning for shared access.

Checklist to launch in 4–8 weeks

1. Define target retention, hot window and compression ratio assumptions.
2. Procure PLC-capable drives or work with a vendor offering PLC server nodes.
3. Implement a compact binary record format with delta encoding; add Parquet/Arrow exporters.
4. Deploy a small hot cache (Redis + ClickHouse hot partition) and measure latency for dashboards.
5. Automate compaction jobs as CronJobs; push outputs to MinIO or object store on PLC nodes.
6. Set up Prometheus/Grafana and automate alerts for PLC telemetry and compaction failures.
7. Run a cost-per-GB and restore-time drill to validate assumptions.

Closing: actionable takeaways

• PLC-backed cold storage paired with a small hot cache gives you strong cost savings for high-frequency commodity data without sacrificing query performance for recent windows.
• Design for append, not random writes — write rolling segments, compact into columnar files, and push to PLC drives in bulk.
• Automate everything: IaC, GitOps, scheduled compaction, telemetry-driven repairs and retention policies.
• Test and measure: compute compression gains, read latency and rebuild times before committing to a large PLC fleet.

If you want a jump-start, build a prototype with one PLC-backed node, a MinIO bucket, a ClickHouse instance for analytics and a small Redis hot cache. Benchmark ingestion and query latencies, then iterate on file segmentation and pre-aggregations.

Call to action

Ready to cut storage costs and run a lean, automated archiver for commodity prices? Download our starter template (infrastructure + ingestion + compactor + analytics) or schedule a workshop to adapt this pattern to your fleet. Email the Passive Cloud engineering team or visit passive.cloud to get the repo and deployment checklist.

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