A/B Testing Assignment Storage vs Event Storage
Compare assignment records and retained event records to understand the main drivers of per-user A/B testing database storage.
A/B testing data usually contains both stable assignment records and repeated event records. This comparison shows how their storage behavior differs and when retention, event frequency, and record size matter most.
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About A/B Testing Assignment Storage vs Event Storage
A/B testing data usually contains both stable assignment records and repeated event records. This comparison shows how their storage behavior differs and when retention, event frequency, and record size matter most.
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Comparisons
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Key Factors
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Low event frequency versus high event frequency
Compare a user with occasional experiment activity to one with frequent recorded exposures.
| Factor | Option A: Low Event Frequency | Option B: High Event Frequency | What It Means |
|---|---|---|---|
| Assignment storage | Similar when active experiment count and assignment size are the same. | Similar when active experiment count and assignment size are the same. | Assignment storage does not depend on daily event frequency. |
| Daily event records | Fewer records created each day. | More records created each day. | Event count is multiplied by active experiments and retention days. |
| Retained event storage | Grows slowly over the retention period. | Grows quickly over the same period. | Higher daily event volume produces more retained records. |
| Sensitivity to event-size changes | Lower absolute impact. | Higher absolute impact. | Every additional byte applies to every stored event. |
| Main planning focus | Validate minimum record and index overhead. | Measure event volume and event payload size. | The useful focus depends on which component dominates the workload. |
When users generate many events, event storage normally becomes much larger than one-time assignment storage.
Short retention versus long retention
Compare storage behavior when event records are retained for different lengths of time.
| Factor | Option A: Short Retention | Option B: Long Retention | What It Means |
|---|---|---|---|
| Assignment storage | Unchanged in this calculator. | Unchanged in this calculator. | The model stores one assignment record per active experiment regardless of event retention. |
| Event storage | Fewer days of records retained. | More days of records retained. | Event storage rises directly with retention days. |
| Storage predictability | Smaller retained dataset. | Larger retained dataset. | With steady event rates, retained storage is easier to project from days and volume in either case. |
| Historical event availability | Less event-level history available in the primary store. | More event-level history available in the primary store. | The appropriate retention period depends on the organization's analytical and operational needs. |
| Impact of inaccurate event-rate estimates | Lower accumulated impact. | Higher accumulated impact. | An event-rate error is multiplied by every retained day. |
Retention affects event data linearly and does not alter assignment storage in the stated model.
Lean records versus rich event payloads
Compare compact records with events that contain more properties or metadata.
| Factor | Option A: Lean Records | Option B: Rich Event Payloads | What It Means |
|---|---|---|---|
| Bytes per assignment or event | Lower average record size. | Higher average record size. | Record size directly scales raw storage. |
| Effect on event storage | Lower storage for every retained event. | Higher storage for every retained event. | The event record size is multiplied by daily event count and retention days. |
| Effect on assignment storage | Lower fixed assignment component. | Higher fixed assignment component. | Assignment size affects one record for each active experiment. |
| Sensitivity to indexes and metadata | May still require meaningful overhead. | May still require meaningful overhead. | Actual overhead depends on the database schema and index design, not payload size alone. |
| Information stored per event | Fewer fields or a more compact representation. | More fields or detailed properties. | The storage trade-off should be considered alongside the intended use of the data. |
Record-size reductions have the greatest storage effect when applied to high-volume retained event data.
Key Differences at a Glance
Assignment storage is a one-time per-experiment component in this model.
Event storage repeats by daily event rate and accumulates across retention days.
Increasing active experiments raises both assignment storage and event storage.
Increasing event record size has a larger absolute effect when event volume is high.
Storage overhead is applied after assignment and event storage are combined.
How to Decide
Assumptions
- Both compared options use the same calculation method and database-overhead concept.
- The comparison describes storage behavior, not database performance or cost.
- Event rates are treated as stable averages across the retention period.
- One average event record size is used for both exposure and conversion records.
Related Comparisons
Frequently Asked Questions
Which uses more A/B testing storage: assignments or events?
Retained events often use more storage when users generate repeated exposures or conversions over many days. The balance depends on event volume, retention, and record sizes.
Does adding an experiment always increase storage?
In this model, yes. It adds one assignment record and also increases daily event storage if the experiment produces stored events.
Is reducing retention more effective than reducing assignment size?
It depends on the data mix, but reducing retention usually has more effect when retained event data is much larger than assignment data.
Does a larger overhead percentage change the assignment-event balance?
No. The same percentage is applied to their combined raw storage, so it increases the total without changing the raw component proportions.
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