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Before choosing a table, partitions, shards, or a database per tenant, write down four storage requirements: how much data will accumulate, how it will be read and written, how long it must remain, and how tenants will grow and be isolated. These answers turn an architectural preference into a decision that can be reviewed and operated.
The four requirements to write down first
- Volume: Estimate rows created per day and the total rows expected by the end of the retention period. Include rows created when a new version replaces an old one; if history is retained, a correction can increase storage rather than overwrite it.
- Access pattern: State the expected read-to-write ratio and identify important query types. A system dominated by writes, frequent lookups, and analytical scans may need different operational priorities.
- Lifetime: Specify how long each row remains and how it is removed. Say whether records share a common expiry boundary or have individual deletion deadlines.
- Tenant growth and isolation: Estimate tenant count and growth, define how tenant data must be separated, and say what isolation is required. Consider what happens if the tenant count multiplies.
These are not just database-sizing notes. Together they constrain the design: a small, bounded dataset may fit one table, while larger or differently retained workloads may justify partitions or another separation strategy. The estimates can be uncertain; making the uncertainty explicit is more useful than leaving the requirement unstated.
Match the requirements to a storage design
Compare options against the same workload, not against a general claim that one architecture is more scalable. The distinctions below describe where complexity tends to live; they do not establish a performance ranking.
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| Option | Where separation happens | Questions to resolve |
|---|---|---|
| One table | Within a single table, without partition boundaries. | Will the data remain small and bounded? Does the access pattern and retention policy remain manageable without table-level separation? |
| Partitions | Within one database, separated into table partitions; the author describes partitions as transparent to ordinary query callers. | Can the partition key support the expected queries and retention process? Is retention uniform enough to remove whole partitions safely? |
| Application-level shards | Across separate databases, with application routing and multi-shard query assembly. | How will requests find the right shard? How will queries spanning shards work, and how will routing change as tenants grow? |
| Database per tenant | At the database boundary for each tenant. | Does the isolation requirement justify the operational and routing burden as tenant count grows? |
Partitions and shards are not interchangeable labels: partitioning keeps the separation inside one database, while sharding makes the application responsible for routing across databases and assembling results. A database per tenant is another isolation choice, not a default consequence of having tenants. In the service described by Anton Brilliantov, hash partitioning by owner was used; application-level sharding and thousands of tenant databases were discussed as possible directions, not as deployed parts of that service (Anton Brilliantov’s article, October 1, 2026).
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Make retention boundaries safe and observable
Partition dropping works best when records can be retired in whole groups. It does not, on its own, meet a rule that gives every individual row a different deletion deadline. If legal or contractual deletion dates vary by record, the removal mechanism must support that granularity.
Brilliantov describes a service with versioned domain data: changes create new rows, current state is derived from history, and domain rows are not physically deleted. In that model, growth is monotonic, including when an incorrect value is superseded. His described retention job drops monthly audit partitions only when they are entirely older than a monthly boundary. The current and next partitions, the default partition, and partitions that cross the boundary are retained. He also describes the job as idempotent and as updating an age gauge even when no partition is dropped. These are details of the author’s example, not universal requirements for a retention worker (Anton Brilliantov’s article, October 1, 2026).
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- Write the retention boundary precisely, including its time unit and the rule for records that cross it.
- Confirm that a partition is wholly eligible before dropping it; do not remove a group that still contains in-retention data.
- Make repeated job runs safe and expose a signal that shows whether the job is keeping up, including when a run has nothing to delete.
- Choose a different mechanism if rows need individual deletion dates rather than shared monthly expiry.
Choose pagination behavior from the query requirement
Large OFFSET pagination can become more expensive as the requested offset grows. It can also be unstable when new inserts shift the page window. Brilliantov describes using keyset pagination, bounded page sizes, and an opt-in total count to avoid those concerns. The trade-off is that keyset pagination does not naturally support jumping directly to an arbitrary page, such as page 4,000. If users need that behavior, treat it as an explicit product and query requirement rather than assuming keyset pagination provides it (Anton Brilliantov’s article, October 1, 2026).
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Partitioning is not automatically useful. If the dataset is small and expected to stay small, one unpartitioned table may be the sounder choice. Partitioning also creates a decision that can be costly to reverse: changing the partition key later can require substantial migration work. Monthly partition removal also imposes month-sized retention granularity, so it is a poor fit when the requirement is to delete each row on its own schedule.
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Estimate volume and retention before choosing, but do not mistake an estimate for a promise of precision. Record the assumptions that drive the design and revisit them when tenant counts, query behavior, or retention rules change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turn each line into a decision and an operational check
For every requirement, record the design consequence and how you will know the mechanism is working. A useful review asks:
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- What daily and retained row counts are expected, and what evidence will show that growth remains within the design assumptions?
- Which read and write workloads need attention or separation?
- Who or what removes expired data, at what boundary, and how will a missed or unsafe deletion be detected?
- How are tenants routed and isolated today, and what changes if their number grows by a multiple?
- Which query and pagination behaviors must the application support?
The numbers in Brilliantov’s article illustrate the scale of one evolving project, not a benchmark or a recommended target: he reports 1,751 lines of code across 35 files and 13 packages on August 10, 2026, compared with 61,411 lines across 1,540 files and 252 packages at a HEAD dated August 16, 2026. He also reports 66 migration files containing 1,990 migration lines and a repository test-to-code ratio rising from 0.45 to 1.25 in the displayed snapshot. These author-reported figures show that storage decisions can accumulate implementation and migration work; they do not prove that a particular design performs better or that the ratio measures software quality (Anton Brilliantov’s article, October 1, 2026).
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