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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsSplitting logs by a business key such as customer ID, user ID, order ID, or trace ID becomes a resource-management problem when the key has many distinct values and each value turns into its own stream, label combination, or partition. Every one of those units has to be indexed, stored, and managed, so the cost grows with the number of values rather than with the number of log lines.
The practical rule is straightforward. Keep stable, bounded attributes as indexed labels or partition keys. Keep high-cardinality identifiers as queryable fields, such as Loki structured metadata, so they can be filtered without creating new streams. Create separate partitions only when groups differ in schema or operational needs such as retention, access patterns, or storage destination.
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What “splitting by business key” actually creates
The phrase covers three different designs, and the cost profile differs for each. A key used to route records is not automatically indexed in the backend, and a key used as a partition or stream identity has very different consequences. Before deciding anything, identify which meaning applies to your pipeline.
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|---|---|---|
| Routing records to different destinations | Separate outputs or destinations defined by pipeline rules | Routing configuration and destination management. The key is not necessarily indexed by the backend. |
| Partitioning into child data streams (Elastic wired streams) | One dedicated child data stream per partition | Each child stream is a separate management object. Elastic’s guidance for this feature is to aim for tens of partitions, not hundreds. That is Elastic’s guidance for wired streams, not a general industry rule. |
| Label or stream identity (Grafana Loki) | One stream for each distinct combination of label names and values | Index size and chunk count. High-cardinality labels can produce a large index and many small chunks. |
Why are my log streams multiplying?
In Loki, cardinality is the number of distinct label combinations, and each combination produces a stream. If a label holds a value that is unique per customer, order, or request, every new value creates new streams. Grafana’s Loki cardinality documentation states that high cardinality can lead to a huge index and many tiny chunks, which reduces performance and cost-effectiveness. The effect is that the index keeps growing while each chunk holds comparatively little data.
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The same pattern appears in Elastic wired streams in a different form. Each partition creates a dedicated child data stream, so a business key with thousands of values used as a partition key multiplies the number of data streams the team must configure, monitor, and govern.
Cardinality is also a question of growth over time. A key with a few hundred stable values behaves differently from one where new values keep arriving, because the second kind continually adds index entries and streams long after the initial deployment.
Where the overhead shows up
- Index growth. Each distinct label combination adds entries the backend must maintain.
- Chunk fragmentation. Many streams with little data each produce many small chunks instead of fewer, fuller ones, which Grafana identifies as a performance and cost concern for Loki.
- Management objects. In Elastic wired streams, every partition is a child data stream with its own lifecycle and governance.
- Operational attention. More streams means more things to troubleshoot when ingestion, querying, or retention behaves unexpectedly.
How these costs translate into hardware, licensing, or query latency depends on the product, its version, and your workload. The sources describe the mechanism, not a measured cost for any particular business-key design.
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Keep the value queryable without making it define the stream
Operators often need to find every log line for a customer, order, or request. That need does not require the identifier to become a stream label. Loki recommends structured metadata for frequently searched high-cardinality values such as customer or transaction IDs. Structured metadata keeps these values available as query filters without increasing label cardinality in the same way.
This distinction is specific to Loki. Other log stores have their own field, index, and mapping models, so confirm how a given backend indexes a field before assuming the same trade-off applies.
When a split is justified
A partition is worth its cost when the groups behind it need different treatment. Elastic’s wired-stream documentation describes these reasons:
- Different schemas or schema mappings. Groups whose fields or mappings differ enough to need separate handling.
- Different retention. One group must be kept longer or deleted sooner than another.
- Different access patterns. Groups are queried by different teams or in fundamentally different ways.
- Different storage destinations. Groups must be written to different storage.
- Different processing. Groups need distinct pipeline treatment.
Each split should map to one of these policies or to a concrete query need. Creating a partition for every entity value fails that test, because the entities usually share the same schema, retention, and access rules.
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“Partition by logical groupings, not by high-cardinality fields.”
That line is from Elastic’s documentation for wired streams.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Tenant isolation is a separate decision
A tenant may be a security boundary, a billing unit, or a workload-isolation requirement. These needs are real, but they are not a reason to turn every business key into a label or partition. Loki supports multi-tenancy to isolate tenant data and workloads, and its shuffle-sharding guidance describes assigning each tenant a subset of queriers to reduce overlap in a shared cluster. Those mechanisms address tenancy and workload isolation. Label choices still determine stream cardinality.
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Preserve context for correlation
OpenTelemetry’s logging specification describes resource context and trace context as ways to correlate logs with other telemetry. It says resource information should be added to collected log data. Elastic’s OpenTelemetry reference architecture describes enriching telemetry with host and Kubernetes resource attributes.
A field can be essential for correlation without being the right index or partition key in a particular backend. Keep the attribute on the record, then decide whether it belongs in an index, in metadata, or nowhere in the indexing layer, based on how the backend stores it and how you query it.
A decision sequence for each business key
- Count the values and measure growth. Determine whether the key is bounded and stable or whether new values keep arriving.
- Decide what the value is for. If it selects a stream or partition, it is an indexing decision. If it only needs to be searched, use a queryable field such as structured metadata where the backend supports it.
- Check whether groups differ operationally. Look for differences in schema, retention, access patterns, storage destination, or processing. If none exist, do not partition.
- Decide tenant boundaries separately. Use the backend’s tenancy controls for isolation, not stream identity.
- Keep correlation context. Retain resource and trace attributes on the records even when they are not indexed.
What the available sources do not establish
- No universal cardinality threshold applies across products and workloads. Any number you see for “safe” stream or partition counts should be read as product-specific guidance.
- No original, dated benchmark was found that quantifies the resource cost of splitting logs by business key across systems.
- Vendor behavior differs. Loki’s label and metadata mechanics and Elastic’s wired-stream partitioning are both documented, but they do not describe one shared model.
Consult the current product documentation for your version before changing an index or partition design, since indexing behavior and limits can change between releases.
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