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SeaCloud Labs moved away from one Elasticsearch index per tenant because the operational work tied to many indexes had become a concern: every index adds shard work and cluster-state metadata. Its alternative, SeaSearch, keeps indexes logically separate but stores their authoritative data in shared S3-compatible storage and routes requests to compute nodes that own the relevant partitions. That changes the tradeoff, rather than eliminating it: ownership changes can avoid moving index data between nodes, but cold reads depend on object storage and local cache behavior.
Why did one index per tenant become a problem?
SeaCloud Labs describes separate indexes as a clean way to isolate tenant data. A request can target the relevant tenant’s index, and one customer’s traffic spike is less likely to affect another. The concern was the overhead of operating a growing number of indexes.
In the company’s account, every index has at least one shard. Each shard maintains Lucene data and performs background work, while mappings and routing information contribute to cluster state replicated across nodes. SeaCloud Labs says mapping updates became sluggish at thousands of indexes and that tens of thousands could make the master node a paging concern. Those are the company’s operational observations, not universal Elasticsearch limits or independently measured thresholds.
There is no document count or index count at which per-tenant indexes automatically become the wrong choice. The relevant pressure depends on index and shard configuration, mapping churn, workload, and the cluster’s ability to manage its state. SeaCloud Labs says it began looking for another approach two years before publishing its account.
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What SeaSearch changes
Rather than build a search engine from scratch, SeaCloud Labs says it used ZincSearch as a base for its Go runtime footprint, Bluge indexing, and Elasticsearch-compatible API, then added shared-storage indexing and routing. SeaSearch still has indexes; its architectural change is where authoritative index data lives and how requests find the compute node responsible for it.
| Concern | Separate index per tenant | SeaSearch approach |
|---|---|---|
| Tenant data | Stored in a tenant-specific index. | Indexes remain separate, with authoritative data in a shared S3-compatible backend. |
| Request scope | A query can target the relevant tenant index. | A proxy routes each request to the compute node that owns the relevant partition. |
| Node changes | Depends on the existing cluster’s recovery and data-placement behavior. | Partition ownership is reassigned; a new owner fetches data from object storage as needed. |
| Persistence and local reads | Index data is managed within the Elasticsearch cluster. | Object storage holds authoritative data; compute nodes use a rotating local-disk cache. |
Metadata, ownership, and routing
SeaSearch hashes indexes into a fixed number of partitions. etcd holds index metadata and the partition-ownership map. A cluster manager monitors node health and assigns ownership, while a proxy or gateway consults the map and forwards client requests to the current owner. Compute nodes serve reads and writes and access the shared object-store bucket.
When nodes change, the ownership map is recomputed. The newly assigned owner fetches the needed data from object storage rather than receiving an authoritative copy migrated from the previous node. SeaCloud Labs summarizes the design this way: “Compute nodes hold no authoritative data, so failover is a map update rather than a data migration.” That describes the ownership mechanism; it does not mean recovery has no latency or that an object-store or metadata-service failure is immaterial.
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Single-node deployment is different
The SeaSearch repository README describes a single-node deployment using bbolt for index metadata and the local filesystem for index data. That is a documented deployment mode, not evidence that a single local disk provides the durability expected from a clustered shared-storage setup.
How the object-store cache affects latency
Shared object storage can simplify persistence and node reassignment, but it can make reads slower when the required data is not already local. SeaCloud Labs says object-store round trips can be roughly an order of magnitude slower than local NVMe. The company does not provide a benchmark method, provider, region, object size, or workload for that comparison, so it should be treated as its characterization of the tradeoff—not a universal latency ratio.
Immutable segments make cached copies manageable
SeaSearch stores index data in immutable segments: a segment can be read or deleted, but it is not modified. Compute nodes keep a rotating local-disk cache and evict older segments when it fills. Since cached segments do not change in place, the same segment can be fetched again from object storage when needed. This arrangement also lets an index be larger than a node’s local disk.
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The company describes parallel segment warm-up and query splitting across nodes as ways to improve cold-path recovery and distribute cache pressure. These mechanisms mitigate cold reads; they do not remove them. A request after a node starts or after relevant segments have been evicted can be slower while data is fetched and cached.
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The active working set matters
SeaCloud Labs says its file-metadata workload works with this tradeoff because the active working set is a small share of the total data. It also cautions that a workload continually reading data uniformly is less suitable: a rotating cache may keep fetching segments that are not reused enough to stay warm. That makes cache hit rate and access distribution central migration questions, not just total index size.
How SeaSearch handles relevance across tenant indexes
BM25 scoring depends on index-local statistics, including term frequency, document count, and average field length. A score produced in a small library and a score from a much larger library are not automatically comparable just because both came from the same query. Merging independently scored results and sorting them as one ranked list can therefore misrepresent relevance.
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SeaSearch’s /api/unified_search endpoint is SeaCloud Labs’ approach to searching across indexes: it accepts an array of index/query pairs and computes comparable scores for the same query across those indexes. Filters can differ by index. This is intended to support a “search everything I can see” experience; it is not a general guarantee that every cross-index ranking problem can be solved by combining scores this way.
What SeaSearch does not replace
Elasticsearch-compatible API does not mean feature parity. SeaCloud Labs lists specific limitations that matter when evaluating an existing application:
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- Supported field types are text, keyword, numeric, bool, date, and vector.
- Mappings can add fields, but cannot change existing fields.
- Unsupported search parameters listed by the company include
indices_boost,knn,min_score,retriever,pit,runtime_mappings,seq_no_primary_term,stats,terminate_after, andversion.
The company explicitly says SeaSearch is not a replacement for observability systems that rely on deep aggregation pipelines and ILM policies. Applications depending on those features, or on other Elasticsearch behavior not listed here, need a compatibility check rather than an assumption based on API resemblance.
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Where this architecture may fit—and what to verify
SeaSearch’s design is most plausible when an installation has many tenant indexes, the active working set is smaller than the full corpus, and a dependable shared object store is available. This is an inference from SeaCloud Labs’ stated tradeoffs and its file-metadata experience, not a published evaluation across workloads.
- Isolation and noisy neighbors: Determine whether separate indexes provide enough tenant scoping, and consider how shared compute and cache demand may affect tenants even when their index data remains separate.
- Index-count operations: Measure shard overhead, cluster-state scale, mapping churn, and the limits of existing operational tooling at your actual tenant count. The company’s examples do not establish a universal cutoff.
- Warm and cold latency: Measure startup and eviction behavior, cache hit rate, and representative access patterns. Include workloads that touch data broadly, not only the hot subset.
- Failure and rebalancing: Compare ownership-map updates and fetching from object storage with replica recovery and data movement in your current architecture. Include object-store and metadata-service failure scenarios.
- API and feature compatibility: Inventory mappings, query parameters, aggregations, vector behavior, lifecycle policies, and integrations before migrating.
- Object-store operations: Validate durability, region, credentials, failure modes, backup and restore, and actual cost for the service you would operate. SeaCloud Labs names S3 and a well-run MinIO cluster as examples, but provides no provider comparison or pricing.
- Ranking behavior: Test whether a consistent query with per-index filters matches the product’s cross-tenant search needs and relevance expectations.
SeaCloud Labs puts the durability boundary plainly: “Your durability is your object store’s durability.” The company cautions against treating a single disk as adequate shared-storage durability. A deployment decision should account for the chosen object store and its operational guarantees, rather than assuming SeaSearch itself supplies a separate durable replica layer.
What the case study establishes—and what it does not
SeaSearch is SeaCloud Labs’ response to the operational burden it encountered with many tenant indexes: keep tenant indexes, place authoritative index data in shared object storage, and route partition ownership through metadata rather than moving authoritative data with compute nodes. The approach exchanges some per-index cluster work for object-store dependency, cache management, and cold-read latency.
The account is a first-party, AI-assisted description of the company’s system and its file-metadata workload, not an independent benchmark. It does not establish that SeaSearch is faster or cheaper for all deployments, specify a universal index-count threshold, or demonstrate suitability for observability workloads centered on deep aggregations and ILM. Project capabilities can also change; check the current project documentation and release before relying on a specific feature.
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