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Valkey's Madelyn Olson says async I/O threading lifted one process to about 1M requests a second
Valkey's async I/O threads lifted throughput per process from about 200,000-250,000 to about one million requests a second, AWS's Madelyn Olson told InfoQ. The replication and slot-migration fixes in the same release line make the firmer case for taking the Redis fork seriously.
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What happened
- Valkey has twelve maintainers and nine technical steering committee members spread across eight companies, among them Ericsson, AWS, Apple and Percona.
- Memory-efficiency work in versions 8.0 and 8.1 targeted key embedding and the per-slot dictionary.
- Version 9 brought numbered databases, the namespaces of single-instance deployments, to cluster mode, removing one reason some users could not move to it.
- Hash-field expiration, one of the project's most requested features according to the interview, also arrived in version 9.
- Glide, a client library that began at AWS, puts connection and topology handling in a shared Rust core exposed through per-language bindings.
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Why it matters
- cost The throughput gain comes from threads, so a process needs spare cores to collect it. Instance sizing built around one busy core per process has to be revisited before the figure means anything.
- constraint Numbered databases remove one blocker to cluster mode, but cross-slot operations still need their data on one shard. Applications built on multi-key operations still need key design work before they migrate.
- exposure The maintainers cite Mastodon-style shared clusters as demand for the version 9 changes but warn the arrangement is not strong multi-tenancy. Isolation between tenants stays with the operator.
- decision Choosing Glide puts one Rust core under every language binding, so a core fix reaches all of them at once and so would a core bug. Native clients, including Go ones, remain supported.
Taken at face value, Olson's figures put the gain at four to five times per process [18]. The interview does not describe the hardware, value sizes, client count or pipelining behind either number [1]. Those variables decide whether a throughput result moves from one team's test setup to another team's cluster. I'd treat the million as a hypothesis to reproduce on production-shaped traffic before it changes a capacity plan.
The control that ships with it is easier to credit. "Unlike the previous I/O-thread arrangement, thread configuration can be changed at runtime, allowing operators to add cores to help handle burst workloads," Olson said [9]. Under the old arrangement the setting could not be changed while the server ran [9]. A setting that moves live can be used during the burst itself.
I think the stronger engineering case is in the replication and cluster work. Before dual-channel replication, a primary syncing a replica sent the full copy of the dataset first and the stream of later changes after it [11]. The 8.x releases send the two in parallel. According to the interview, that reduces memory pressure on the primary during synchronisation [11]. Presumably the saving comes from no longer holding the changes made during the copy on the primary until the copy completes.
Atomic slot migration in version 9 closes a failure mode in resharding. With the old key-by-key method, a failed operation could stop a cluster with only part of its data moved [12]. The new method stages the data in advance and then cuts over, so a migration either completes in full or does not happen at all [12]. I suspect most operators assumed they already had that behaviour.
The project is more than two years past the fork from Redis [7]. Both developers InfoQ interviewed work at companies among the eight that supply its maintainers [8]. Olson works at AWS, sits on the technical steering committee and was a Redis committer before the split [16]. "I helped create the fork after the license change," she said [16]. Viktor Soderqvist works on open source at Ericsson and was also involved in creating the fork [17].
What to watch
- Synchronous replication: the maintainers propose it to give durability guarantees for data that is not ephemeral, and shipping it would take Valkey past caching.
- The tiering design, which would keep hot data in DRAM, move colder data to SSD, and possibly use a general interface that could reach S3 or Postgres.
- An independent reproduction of the one-million-requests figure that publishes hardware, value sizes and client counts.
Clarity's read
What the record supports and how the coverage leans. The claims behind it follow.
Reality
- Evidence45
- Adoption
- Insufficient
- Hype gap+20
- Incentives65
- Confidence50
Claim ledger
Ranked by verification strength, evidence, and original report placement.
- [1]
"Async I/O threading increased throughput from approximately 200,000-250,000 requests per second to around one million per process."
ReportedSupportedSource: Madelyn Olson, Valkey TSC member at AWS, interviewed by InfoQ2 sources— create a free account to open themView cited source - [2]
Cluster mode distributes keys by slot across nodes, and cross-slot operations require the data to reside on the same shard.
ReportedSupportedSource: Madelyn Olson, interviewed by InfoQ2 sources— create a free account to open themView cited source - [3]
Version 9 added numbered databases, the namespaces available in single-instance deployments, to cluster mode, removing one reason some users could not migrate to cluster mode.
ReportedSupportedSource: Madelyn Olson, interviewed by InfoQ2 sources— create a free account to open themView cited source - [4]
The interviewees cited Mastodon-style deployments, multiple servers using one cluster, as an example of demand for the cluster changes, while cautioning against treating that arrangement as strong multi-tenancy.
ReportedSupportedSource: Olson and Soderqvist, interviewed by InfoQ2 sources— create a free account to open themView cited source - [5]
Proposed directions beyond caching include synchronous replication, aimed at durability guarantees for data that is not merely ephemeral, and tiering that keeps hot data in memory while moving colder data to SSD or other storage.
ReportedSupportedSource: Olson and Soderqvist, interviewed by InfoQ2 sources— create a free account to open themView cited source - [6]
The team is considering a more general tiering interface that could support backends beyond local NVMe, such as S3 or Postgres.
ReportedSupportedSource: Madelyn Olson, interviewed by InfoQ2 sources— create a free account to open themView cited source - [7]
More than two years after the fork from Redis, Valkey looks like a young but growing project that aims to gain more momentum.
- [8]
Valkey has twelve maintainers and nine technical steering committee members spread across eight companies, among them Ericsson, AWS, Apple and Percona.
- [9]
"Unlike the previous I/O-thread arrangement, thread configuration can be changed at runtime, allowing operators to add cores to help handle burst workloads."
- [10]
Versions 8.0 and 8.1 included memory-efficiency work involving key embedding and the per-slot dictionary.
- [11]
Dual-channel replication addressed pressure on a primary during replica synchronisation. Previously the full copy of the dataset and the subsequent stream of replication changes were sent sequentially; sending them in parallel reduces the associated memory pressure. This was part of the 8.x releases.
- [12]
Moving data key by key could leave a cluster partially migrated if the operation failed. Version 9's atomic slot migration pre-stages the data and then switches over, so the migration either takes effect or does not.
- [13]
Version 9 introduced hash-field expiration, described as one of the project's most requested features.
- [14]
Glide, which began as an AWS project, puts connection and topology handling in a shared Rust core exposed through language-specific bindings, so a bug fixed in the core is fixed for all bindings that use it.
- [15]
Glide is not intended to eliminate native alternatives; Valkey continues to support other clients, including Go implementations, for users whose needs are already met by them.
- [16]
"I helped create the fork after the license change." Olson works at AWS, serves on Valkey's technical steering committee and was previously a Redis committer.
- [17]
Viktor Soderqvist works on open source at Ericsson and was also involved in creating the fork.
- [18]
Olson's figures imply a per-process throughput gain of four to five times.
Sources
1 independent publisher whose own reporting we read for this story.
- infoq.comOlson and Söderqvist Discuss Valkey’s Evolution and Future Past Caching Use Cases at OSS EU
1 article · October 9, 2026
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