When Constant Polling Meets Finite Storage: How Partitioning by Workload Type Solved Our Scaling Nightmare
When the AdTech Monitoring System Hit a Wall: Maya's Story
Maya was the lead platform engineer at a mid-stage adtech startup. Their system collected bid responses, campaign events, and system metrics at high velocity. Engineers assumed the simplest path: poll everything frequently, store raw events, and build dashboards later. It felt safe. After all, disk is cheap, right?
But one month, after a spike in traffic, the team received an alert: storage costs had skyrocketed and several indices were unresponsive. Log retention policies were ignored for some critical streams. Meanwhile, dashboards lagged behind real-time by minutes, and on-call pages were triggered by slow queries. This was not a gradual degradation. It was a sudden, painful collision between assumptions and reality.
What Maya discovered in the weeks that followed changed how the team thought about data storage: not all data is the same, and treating all workloads as identical leads to wasted capacity, expensive I/O, and brittle systems.
The Hidden Cost of Treating All Data Like It’s EqualWhy did constant polling feel like the right answer? It reduced complexity up front. Teams could build simple collectors that poll sources frequently, write every record to the same store, and run queries against a single index. The mental model is straightforward. But the costs manifest in three ways:
Storage growth that outpaces value: raw events, backfill windows, and duplicates balloon capacity needs. Query contention: analytical scans compete with ingestion, degrading tail latency. Operational overhead: compaction, index rebuilding, and retention enforcement become full-time work.As it turned out, most of the traffic was not uniformly useful. High-frequency polling created a flood of short-lived, high-churn records that deserved different handling than long-lived audit logs or aggregated metrics.
Ask yourself: which data do you actually need instant access to, and which can be summarized, delayed, or sampled? How many systems are you forcing to behave the same way because you never separated workloads?
Why Simple Centralized Storage and Frequent Polling Often FailThere is a straightforward reason simple architectures collapse: different workload types have orthogonal requirements. Batch analytics cares about throughput and sequential reads. Real-time controllers care about low tail latency and small writes. Time-series metrics need efficient downsampling. Yet constant polling collapses all workloads into one path.
Consider common failure modes we've seen:
Hot partitions: high write rates concentrate data on a small subset of files or shards, causing GC storms and slow compactions. Index blowup: keeping secondary indices for everything creates write amplification and increased disk usage. Retention friction: ad hoc retention policies mean some systems keep data far longer than necessary, because deleting at scale is expensive.Why don’t simple mitigations fix these? You might try compression, more indexing, or a larger cluster. Those help briefly. This led to higher costs and complexity without addressing the core mismatch: workload characteristics were still merged together.
How Maya Shifted from Polling-everything to Partitioning by Workload TypeMaya pushed for a different approach: partition storage and processing by workload type. Instead of one monolithic data path, she proposed separate pipelines tuned to the semantics of each class of data. This was not a marketing-driven architecture exercise. It started with a spreadsheet mapping workload properties to operational requirements:
Access pattern: read-heavy vs write-heavy Retention needs: seconds, days, months, years Query characteristics: point lookups, time-range scans, ad-hoc joins Durability and consistency requirements Cost sensitivity and SLOsShe then categorized data into clear workload buckets:
Click here for more Short-lived telemetry and heartbeat data - high ingestion, low retention Latency-sensitive control events - small writes, requires quick reads Analytical event streams - large volumes, read-in-batch Audit and regulatory logs - long retention, infrequent readsQuestion: what would your data look like if you mapped each stream to these attributes? Could you delete, downsample, or move some of it to cheaper storage?
Design principles Maya applied Use right-sized stores: stores optimized for the workload instead of forcing one system to do everything. Push processing upstream: summarize or deduplicate at ingestion for high-churn data. Apply retention at the partition level: short-lived data should be cheap and ephemral; long-lived data should be compact and immutable. Isolate hot paths: separate the latency-sensitive control plane from analytics so each can meet its SLOs independently.As it turned out, this partitioning reduced write amplification, simplified retention management, and allowed targeted indexing strategies per workload.
Why Many Quick Fixes Don’t Address Root CausesTeams often try incremental fixes: add more RAM, increase cluster size, or shard more aggressively. Those moves can postpone failure, but they rarely remove the root cause: conflated workload semantics. Here are common traps:
Adding cache layers without changing retention logic - you still fill cold storage with transient data. Creating more shards but keeping a single replication strategy - you multiply overhead. Relying on a single database type to serve both online and analytical queries - you end up tuning for neither.Why do teams fall into these traps? Often because constant polling feels safe and flexible. Polling simplifies ingestion because receivers always "have the latest" copy. But at scale, frequent polling produces redundant work and storage churn.
Question: how much of your ingestion traffic is repeat reads that could be avoided with event-driven or push-based designs?
How the Team Built Workload-Specific Storage PathsMaya and her team executed a pragmatic rollout. They did not rip everything out at once. Instead, they introduced workload-specific pipelines incrementally.
Step 1: Identify and profile workloadsThey instrumented producers to label messages with a workload tag. Then they ran a two-week profile to measure write rates, size distributions, and query patterns. This profiling revealed that 60 percent of ingress volume was high-churn telemetry that was only useful for one hour.
Step 2: Introduce a streaming layer for real-time and high-churn dataFor short-lived telemetry, they introduced a lightweight streaming pipeline that accepted events, performed deduplication and downsampling, and wrote summary aggregates to a time-series store. Raw events were kept in cheaper object storage for a limited window.
Step 3: Keep latency-sensitive control events in a low-latency storeControl events were routed to a key-value store optimized for small reads and writes. Replication and reads were tuned to meet strict SLOs. This reduced tail latency compared with an analytical store handling mixed workloads.
Step 4: Send analytics traffic to batch-optimized systemsAnalytical event streams were written to append-only object storage and a columnar analytic engine was used for heavy scans. Nightly compaction jobs reduced file counts and optimized query performance.
This led to a meaningful cleanup: the analytic store no longer contended with low-latency writes, and the real-time path had predictable tail latency because its writes and reads were isolated.
From Unbounded Storage to Predictable Costs: The ResultsThe transformation was substantial and measurable. Within three months the team reported:
40-60 percent reduction in storage cost for active data, because high-churn events were summarized and moved to cheaper tiers. Less than 10 percent of queries suffering timeouts, down from 35 percent, as analytical jobs no longer hit the real-time cluster. Simpler retention enforcement: each partition had a clear policy, making deletions and lifecycle management straightforward.From $120k per month down to $68k, the CFO was happy. But the bigger win was operational: on-call fatigue decreased and the team had time to focus on product features rather than firefighting slow queries.
Question: what would a 40 percent reduction in storage costs buy your team? More headroom for growth, or budget for other projects?
Expert-Level Guidance: When and How to Partition by Workload TypePartitioning is not just about moving data. It is about matching storage primitives to workload semantics. Here are practical rules of thumb:
Segment by access lifetime: keep hot writes separate from cold archives. Choose storage that matches query shapes: time-series DBs for time-window scans, column stores for large scan aggregations, KV stores for point reads. Enforce retention at the ingestion boundary: drop or summarize data early when possible. Implement backpressure and batching: reduce write amplification by grouping small writes. Use object storage for append-only, seldom-read data and reserve database storage for frequently accessed datasets.Do you need strict consistency for all streams? Probably not. Relaxing consistency where acceptable buys you efficiency and cheaper replication strategies.
Metrics to watch Write amplification and compaction time Tail latency percentiles for each workload partition Storage cost per GB per workload Read-to-write ratio and bytes scanned per query Retention compliance and deletion lag Tools and Resources for Implementing Partitioned StorageNo single product solves every problem. Mix and match components based on workload needs. Useful tools and patterns include:
WorkloadExample ToolsWhy High-churn telemetryKafka, Pulsar + S3 (short-term), TimescaleDB (aggregates)Streaming for ingestion, object store for cheap short-term retention, time-series DB for queries Latency-sensitive controlRedis, DynamoDB, CassandraLow-latency KV stores with tunable consistency Analytical event storeClickHouse, BigQuery, Snowflake, Parquet on S3Columnar formats and batch engines for scans Audit logsS3 + Glacier, MinIOImmutable, low-cost object storage with lifecycle policies Monitoring/metricsPrometheus, VictoriaMetrics, MimirPurpose-built for time-series retention and downsamplingAlso consider orchestration and observability tools: Fluentd/Fluent Bit for routing, Grafana for visibility, and data cataloging tools to maintain schema awareness across partitions.
Common Questions Teams AskHow do you migrate without disrupting production? Start with noncritical streams. Route copies to the new pipeline and compare results before cutting over.
How do you enforce retention consistently? Apply lifecycle policies at storage and ingestion boundaries. Make retention part of the schema: tag data with retention_class and enforce with automated jobs.
What about indexing trade-offs? Index only what you need. Secondary indices are expensive; consider sparse indices or precomputed lookup tables for common patterns.
How much does this add operational complexity? Some complexity is deliberate and pays for itself. The alternative is uncontrolled complexity from a single overloaded system. Partitioning makes reasoning about failure modes easier.
Final Thoughts: Question Assumptions, Especially the Comfortable OnesConstant polling and the "throw-more-storage-at-it" instinct are comfortable because they delay hard decisions. But storage is not unlimited, and treating all data as if it must be retained and queried the same way is expensive and fragile.
Maya's team moved from reactive firefighting to intentional design by partitioning by workload type. This did not eliminate work. It changed the work to where it mattered: defining clear semantics for each class of data, selecting appropriate storage, and enforcing lifecycles that align with business needs.
Ask yourself: what assumptions are baked into your ingestion pipeline? Which data streams could be summarized, delayed, or moved to a cheaper tier? What would happen if you stopped polling some sources as often?
Start small. Profile. Isolate the worst offenders. You will be surprised how much unnecessary storage vanishes once you stop treating everything as if it had the same value and access pattern.