Performance and Scalability
Istio makes it easy to create a network of deployed services with rich routing, load balancing, service-to-service authentication, monitoring, and more - all without any changes to the application code. Istio strives to provide these benefits with minimal resource overhead and aims to support very large meshes with high request rates while adding minimal latency.
The Istio data plane components, the Envoy proxies, handle data flowing through the system. The Istio control plane component, Istiod, configures the data plane. The data plane and control plane have distinct performance concerns.
Performance summary for Istio 1.7.3
The Istio load tests mesh consists of 1000 services and 2000 sidecars with 70,000 mesh-wide requests per second. After running the tests using Istio 1.7.3, we get the following results:
- The Envoy proxy uses 0.5 vCPU and 50 MB memory per 1000 requests per second going through the proxy.
- Istiod uses 1 vCPU and 1.5 GB of memory.
- The Envoy proxy adds 2.76 ms to the 90th percentile latency.
Control plane performance
Istiod configures sidecar proxies based on user authored configuration files and the current state of the system. In a Kubernetes environment, Custom Resource Definitions (CRDs) and deployments constitute the configuration and state of the system. The Istio configuration objects like gateways and virtual services, provide the user-authored configuration. To produce the configuration for the proxies, Istiod processes the combined configuration and system state from the Kubernetes environment and the user-authored configuration.
The control plane supports thousands of services, spread across thousands of pods with a similar number of user authored virtual services and other configuration objects. Istiod’s CPU and memory requirements scale with the amount of configurations and possible system states. The CPU consumption scales with the following factors:
- The rate of deployment changes.
- The rate of configuration changes.
- The number of proxies connecting to Istiod.
however this part is inherently horizontally scalable.
When namespace isolation is enabled, a single Istiod instance can support 1000 services, 2000 sidecars with 1 vCPU and 1.5 GB of memory. You can increase the number of Istiod instances to reduce the amount of time it takes for the configuration to reach all proxies.
Data plane performance
Data plane performance depends on many factors, for example:
- Number of client connections
- Target request rate
- Request size and Response size
- Number of proxy worker threads
- CPU cores
- Number and types of proxy filters, specifically telemetry v2 related filters.
The latency, throughput, and the proxies’ CPU and memory consumption are measured as a function of said factors.
CPU and memory
Since the sidecar proxy performs additional work on the data path, it consumes CPU and memory. As of Istio 1.7, a proxy consumes about 0.5 vCPU per 1000 requests per second.
The memory consumption of the proxy depends on the total configuration state the proxy holds. A large number of listeners, clusters, and routes can increase memory usage. Istio 1.1 introduced namespace isolation to limit the scope of the configuration sent to a proxy. In a large namespace, the proxy consumes approximately 50 MB of memory.
Since the proxy normally doesn’t buffer the data passing through, request rate doesn’t affect the memory consumption.
Since Istio injects a sidecar proxy on the data path, latency is an important consideration. Istio adds an authentication filter and a telemetry filter and a metadata exchange filter to the proxy. Every additional filter adds to the path length inside the proxy and affects latency.
The Envoy proxy collects raw telemetry data after a response is sent to the client. The time spent collecting raw telemetry for a request does not contribute to the total time taken to complete that request. However, since the worker is busy handling the request, the worker won’t start handling the next request immediately. This process adds to the queue wait time of the next request and affects average and tail latencies. The actual tail latency depends on the traffic pattern.
Note: in Istio release 1.7, we are introducing a new way of measuring performance by enabling
the load generator. It helps by modeling random traffic from the client side when using connection pools. In the next
section, we will present both
non-jitter performance measurements.
Latency for Istio 1.7.3
Inside the mesh, a request traverses the client-side proxy and then the server-side
proxy. In the default configuration of Istio 1.7.3 (i.e. Istio with telemetry v2),
the two proxies add about 2.76 ms and 2.88 ms to the 90th and 99th percentile latency, respectively, over the baseline data plane latency.
jitter, those numbers reduced to 1.72 ms and 1.91 ms, respectively. We obtained these results using the Istio benchmarks
http/1.1 protocol, with a 1 kB payload at 1000 requests per second using 16 client connections, 2 proxy workers and mutual TLS enabled.
baselineClient pod directly calls the server pod, no sidecars are present.
none_bothIstio proxy with no Istio specific filters configured.
v2-stats-wasm_bothClient and server sidecars are present with telemetry v2
v2-stats-nullvm_bothClient and server sidecars are present with telemetry v2
nullvmconfigured by default.
v2-sd-full-nullvm_bothExport Stackdriver metrics, access logs and edges with telemetry v2
v2-sd-nologging-nullvm_bothSame as above, but does not export access logs.
Istio uses the following tools for benchmarking