Performance Tuning Guide
Tweaking your Jaeger instance to achieve a better performance
Jaeger was built from day 1 to be able to ingest huge amounts of data in a resilient way. To better utilize resources that might cause delays, such as storage or network communications, Jaeger buffers and batches data. When more spans are generated than Jaeger is able to safely process, spans might get dropped. However, the defaults might not fit all scenarios: for instance, Agents running as a sidecar might have more memory constraints than Agents running as a daemon in bare metal.
Although performance tuning the individual components is important, the way Jaeger is deployed can be decisive in obtaining optimal performance.
Scale the Collector up and down
Use the auto-scaling capabilities of your platform: the Collector is nearly horizontally scalable so that more instances can be added and removed on-demand. A good way to scale up and down is by checking the
jaeger_collector_queue_length metric: add instances when the length is higher than 50% of the maximum size for extended periods of time. Another metric that can be taken into consideration is
jaeger_collector_in_queue_latency_bucket, which is a histogram indicating how long spans have been waiting in the queue before a worker picked it up. When the queue latency gets higher over time, it’s a good indication to increase the number of the workers, or to improve the storage performance.
Adding Collector instances is recommended when your platform provides auto-scaling capabilities, or when it’s easier to start/stop Collector instances than changing existing, running instances. Scaling horizontally is also indicated when the CPU usage should be spread across nodes.
Make sure the storage can keep up
Each span is written to the storage by the Collector using one worker, blocking it until the span has been stored. When the storage is too slow, the number of workers blocked by the storage might be too high, causing spans to be dropped. To help diagnose this situation, the histogram
jaeger_collector_save_latency_bucket can be analyzed. Ideally, the latency should remain the same over time. When the histogram shows that most spans are taking longer and longer over time, it’s a good indication that your storage might need some attention.
Place the Agents close to your applications
The Agent is meant to be placed on the same host as the instrumented application, in order to avoid UDP packet loss over the network. This is typically accomplished by having one Agent per bare metal host for traditional applications, or as a sidecar in container environments like Kubernetes, as this helps spread the load handled by Agents with the additional advantage of allowing each Agent to be tweaked individually, according to the application’s needs and importance.
Consider using Apache Kafka as intermediate buffer
Jaeger can use Apache Kafka as a buffer between the Collector and the actual backing storage (Elasticsearch, Apache Cassandra). This is ideal for cases where the traffic spikes are relatively frequent (prime time traffic) but the storage can eventually catch up once the traffic normalizes. For that, the
SPAN_STORAGE_TYPE environment variable should be set to
kafka in the Collector and the Jaeger Ingester component can be used, reading data from Kafka and writing it to the storage.
In addition to the performance aspects, having spans written to Kafka is useful for building real time data pipeline for aggregations and feature extraction from traces.
Client (Tracer) settings
The Jaeger Clients are built to have minimal effect to the instrumented application. As such, it has conservative defaults that might not be suitable for all cases. Note that Jaeger Clients can be configured programmatically or via environment variables.
Adjust the sampling configuration
JAEGER_SAMPLER_PARAM specify how often traces should be “sampled”, ie, recorded and sent to the Jaeger backend. For applications generating a large number of spans, setting the sampling type to
probabilistic and the value to
0.001 (the default) will cause traces to be reported with a 1/1000th chance. Note that the sampling decision is made at the root span and propagated down to all child spans.
For applications with low to medium traffic, setting the sampling type to
const and value to
1 will cause all spans to be reported. Similarly, tracing can be disabled by setting the value to
0, while context propagation will continue to work.
Some Clients support the setting
JAEGER_DISABLED to completely disable the Jaeger Tracer. This is recommended only if the Tracer is behaving in a way that causes problems to the instrumented application, as it will not propagate the context to the downstream services.
remotesampling strategy, so that admins can centrally set the concrete sampling strategy for each service.
Increase in-memory queue size
Most of the Jaeger clients, such as the Java, Go, and C# clients, buffer spans in memory before sending them to the Jaeger Agent/Collector. The maximum size of this buffer is defined by the environment variable
JAEGER_REPORTER_MAX_QUEUE_SIZE (default value: about
100 spans): the larger the size, the higher the potential memory consumption. When the instrumented application is generating a large number of spans, it’s possible that the queue will be full causing the Client to discard the new spans (metric
In most common scenarios, the queue will be close to empty (metric:
jaeger_tracer_reporter_queue_length), as spans are flushed to the Agent or Collector at regular intervals or when a certain size of the batch is reached. The detailed behavior of this queue is described in this GitHub issue.
Modify the batched spans flush interval
The Java, Go, NodeJS, Python and C# Clients allow the customization of the flush interval (default value:
1000 milliseconds, or 1 second) used by the reporters, such as the
RemoteReporter, to trigger a
flush operation, sending all in-memory spans to the Agent or Collector. The lower the flush interval is set to, the more frequent the flush operations happen. As most reporters will wait until enough data is in the queue, this setting will force a flush operation at periodic intervals, so that spans are sent to the backend in a timely fashion.
When the instrumented application is generating a large number of spans and the Agent/Collector is close to the application, the networking overhead might be low, justifying a higher number of flush operations. When the
HttpSender is being used and the Collector is not close enough to the application, the networking overhead might be too high so that a higher value for this property makes sense.
Jaeger Agents receive data from Clients, sending them in batches to the Collector. When not properly configured, it might end up discarding data even if the host machine has plenty of resources.
Adjust server queue sizes
The set of “server queue size” properties (
processor.zipkin-compact.server-queue-size) indicate the maximum number of span batches that the Agent can accept and store in memory. It’s safe to assume that
jaeger-compact is the most important processor in your Agent setup, as it’s the only one available in most Clients, such as the Java and Go Clients.
The default value for each queue is
1000 span batches. Given that each span batch has up to 64KiB worth of spans, each queue can hold up to 64MiB worth of spans.
In typical scenarios, the queue will be close to empty (metric
jaeger_agent_thrift_udp_server_queue_size) as span batches should be quickly picked up and processed by a worker. However, sudden spikes in the number of span batches submitted by Clients might occur, causing the batches to be queued. When the queue is full, the older batches are overridden causing spans to be discarded (metric
Adjust processor workers
The set of “processor workers” properties (
processor.zipkin-compact.workers) indicate the number of parallel span batch processors to start. Each worker type has a default size of
10. In general, span batches are processed as soon as they are placed in the server queue and will block a worker until the whole packet is sent to the Collector. For Agents processing data from multiple Clients, the number of workers should be increased. Given that the cost of each worker is low, a good rule of thumb is 10 workers per Client with moderate traffic: given that each span batch might contain up to 64KiB worth of spans, it means that 10 workers are able to send about 640KiB concurrently to a Collector.
The Collector receives data from Clients and Agents. When not properly configured, it might process less data than what would be possible on the same host, or it might overload the host by consuming more memory than permitted.
Adjust queue size
Similar to the Agent, the Collector is able to receive spans and place them in an internal queue for processing. This allows the Collector to return immediately to the Client/Agent instead of waiting for the span to make its way to the storage.
2000) dictates how many spans the queue should support. In the typical scenario, the queue will be close to empty, as enough workers should exist picking up spans from the queue and sending them to the storage. When the number of items in the queue (metric
jaeger_collector_queue_length) is permanently high, it’s an indication that either the number of workers should be increased or that the storage cannot keep up with the volume of data that it’s receiving. When the queue is full, the older items in the queue are overridden, causing spans to be discarded (metric
Given that the queue size should be close to empty most of the time, this setting should be as high as the available memory for the Collector, to provide maximum protection against sudden traffic spikes. However, if your storage layer is under-provisioned and cannot keep up, even a large queue will quickly fill up and start dropping data.
Adjust processor workers
Items from the span queue in the Collector are picked up by workers. Each worker picks one span from the queue and persists it to the storage. The number of workers can be specified by the setting
50) and should be as high as needed to keep the queue close to zero. The general rule is: the faster the backing storage, the lower the number of workers can be. Given that workers are relatively cheap, this number can be increased at will. As a general rule, one worker per 50 items in the queue should be sufficient when the storage is fast. With a
2000, having about
40 workers should be sufficient. For slower storage mechanisms, this ratio should be adjusted accordingly, having more workers per queue item.