แก้ไข

แชร์ผ่าน

Scaling

  • บทความ

Scaling under load

While scaling a cache under load, configure your maxmemory-reserved setting to improve system responsiveness. For more information, see Configure your maxmemory-reserved setting.

Scaling clusters

Try reducing data as much as you can in the cache before scaling your clustered cache in or out. Reducing data ensures smaller amounts of data have to be moved, which reduces the time required for the scale operation. For more information on when to scale, see When to scale.

Scale before load is too high

Start scaling before the server load or memory usage gets too high. If it's too high, that means Redis server is busy. The busy Redis server doesn't have enough resources to scale and redistribute data.

Cache sizes

If you're using TLS and you have a high number of connections, consider scaling out so that you can distribute the load over more cores. Some cache sizes are hosted on VMs with four or more cores. By distributing the workloads across multiple cores, you help bring down overall CPU usage on the cache VMs. For more information, see details around VM sizes and cores.

Scaling and memory

You can scale your cache instances in the Azure portal. Also, you can programmatically scale your cache using PowerShell cmdlets, Azure CLI, and by using the Microsoft Azure Management Libraries (MAML).

When you scale a cache up or down in the portal, both maxmemory-reserved and maxfragmentationmemory-reserved settings automatically scale in proportion to the cache size. For example, if maxmemory-reserved is set to 3 GB on a 6-GB cache, and you scale to 12-GB cache, the settings automatically updated to 6 GB during scaling. When you scale down, the reverse happens.

When you scale a cache up or down programmatically, using PowerShell, CLI or Rest API, any maxmemory-reserved or maxfragmentationmemory-reserved are ignored as part of the update request. Only your scaling change is honored. You can update these memory settings after the scaling operation has completed.

For more information on scaling and memory, depending on your tier see either:

Note

When you scale a cache up or down programmatically, any maxmemory-reserved or maxfragmentationmemory-reserved are ignored as part of the update request. Only your scaling change is honored. You can update these memory settings after the scaling operation has completed.

Minimizing your data helps scaling complete quicker

If preserving the data in the cache isn't a requirement, consider flushing the data prior to scaling. Flushing the cache helps the scaling operation complete more quickly so the new capacity is available sooner. See more details on how to initiate flush operation.

Scaling Enterprise tier caches

Because the Enterprise and Enterprise Flash tiers are built on Redis Enterprise rather than open-source Redis, there are some differences in scaling best practices. See Best Practices for the Enterprise and Enterprise Flash tiers for more information.

Next steps