Overview

This directory contains scripts used to run a DNS performance test in a Kubernetes cluster. The performance script run benchmarks the performance of a single DNS server instance with a synthetic query workload.

Quick start

Prerequisites

This assumes you have a working kubectl command Kubernetes cluster. The Python code depends on the numpy package, which is available as python-numpy on Debian-based systems or with pip install.

Running a performance test

$ mkdir out/                                        # output directory
$ ./run --params params/default.yaml --out-dir out  # run the perf test

run will run a performance benchmark ranging over the parameters given in --params. The included default.yaml run will take several hours to run through all combinations. Each run will create a run-<timestamp> directory under the output directory. latest symlink will point to the latest run directory that was created.

Benchmarking the cluster DNS

You can benchmark the existing cluster DNS by specifying the --use-cluster-dns flag. (As opposed to the server referenced by --deployment-yaml). Note: you should be aware that some noise may be introduced if the client runs on the same pod as a DNS server.

Note: test parameters such as resource limits do not apply when testing the cluster DNS as they cannot be changed. The run script will skip these parameters when running in this mode. (See params.Param.is_relevant() for details).

Analyzing results

Use the ingest script to parse the results of the runs into a sqlite3 database.

$ ./ingest --db out/db out/latest/*.out

The resulting metrics can then be queried using sqlite3. The schema of the database can be shown using sqlite3 out/db ".schema". To run sql queries, you can use sqlite3 out/db < my-query.sql or sqlite3 out/db "select * from runs" directly.

Example queries

Maximum 99th percentile latency with dnsmasq caching disabled:

SELECT
  max(latency_99_percentile)
FROM
  results NATURAL JOIN runs -- equijoin on run_id, run_subid
WHERE
  dnsmasq_cache = 0;

Runs that have 95th percentile latency less than 20 ms:

SELECT
  run_id, run_subid, dnsmasq_cpu, kubedns_cpu, max_qps, query_file,
  '--',
  qps, latency_95_percentile
FROM
  results NATURAL JOIN runs
WHERE
  results.latency_95_percentile < 20 -- milliseconds
  AND results.run_id = runs.run_id
  AND results.run_subid = runs.run_subid
ORDER BY
  qps ASC;

Additional sql queries can be found in sql/.

Monitoring

Kubernetes kube-dns v1.5+ (image gcr.io/google_containers/kubedns-amd64:1.9) now exports Prometheus metrics by default. A sample prometheus pod that scrapes kube-dns metrics is defined in cluster/prometheus.yaml and can be created using kubectl:

$ kubectl create -f cluster/prometheus.yaml

Key metrics to look at are:

• dnsmasq\_cache\_hits, dnsmasq\_cache\_misses - number of DNS requests to the caching layer. Note: dnsmasq\_cache\_hits + dnsmasq\_cache\_misses = total DNS QPS.
• skydns\_skydns\_request\_duration\_seconds\_count - total number of requests served by the kube-dns component.

Details

Methodology

The questions we want to answer:

• What is the maximum queries per second (QPS) we can get from the Kubernetes DNS service given no limits?
• If we restrict CPU resources, what is the performance we can expect? (i.e. resource limits in the pod yaml).
• What are the SLOs (e.g. query latency) for a given setting that the user can expect? Alternate phrasing: what can we expect in realistic workloads that do not saturate the service?

The inclusion of max_qps vs attained qps is to answer the third question. For example, if a user does not hit the maximum QPS possible from a given DNS server pod, then what are the latencies that they should expect? Latency increases with load and if a user's applications do not saturate the service, they will attain better latencies.

Parameters

The performance test harness tests all combinations of the parameters given in the --params file. For example, the yaml file below will test all combinations of run_length_seconds, kubedns_cpu, dnsmasq_cpu, ..., query_file, resulting in 1 * 4 * 5 * 2 * 5 * 4 = 800 combinations.

# Number of seconds to run with a particular setting.
run_length_seconds: [60]
# cpu limit for kubedns, null means unlimited.
kubedns_cpu: [200, 250, 300, null]
# cpu limit for dnsmasq, null means unlimited.
dnsmasq_cpu: [100, 150, 200, 250, null]
# size of dnsmasq cache. Note: 10000 is the maximum. 0 to disable caching.
dnsmasq_cache: [0, 10000]
# Maximum QPS for dnsperf. dnsperf is self-pacing and will ramp request rate
# until requests are dropped. null means no limit.
max_qps: [500, 1000, 2000, 3000, null]
# File to take queries from. This is in dnsperf format.
query_file: ["nx-domain.txt", "outside.txt", "pod-ip.txt", "service.txt"]

Results schema

CREATE TABLE runs (
  run_id,
  run_subid,
  run_length_seconds,
  dnsmasq_cpu,
  dnsmasq_cache,
  kubedns_cpu,
  max_qps,
  query_file,
  primary key (run_id, run_subid)
);

CREATE TABLE results (
  run_id,
  run_subid,
  queries_sent,
  queries_completed,
  queries_lost,
  run_time,
  qps,
  avg_latency,
  min_latency,
  max_latency,
  stddev_latency,
  latency_50_percentile,          -- in milliseconds
  latency_95_percentile,
  latency_99_percentile,
  latency_99_5_percentile,
  primary key (run_id, run_subid)
);

CREATE TABLE histograms (
  run_id,
  run_subid,
  rtt_ms,
  rtt_ms_count
);

Customizing and extending

Using the cluster DNS server configuration

kube-dns is installed by default using addon-manager. The deployment configuration is located in /etc/kubernetes/addons/dns. You can use the deployment yaml from this directory as the argument to --deployment-yaml above, however, you will need to replace the k8s-app: kube-dns label and replace it with app: kube-dns-perf-server to avoid clashing with the system DNS.

Using a different DNS server

You can give different DNS server yaml to the runner via the --deployment-yaml flag. Note: test parameters such as kubedns_cpu etc may no longer make sense, so they should be removed from the --params file when the test is run.

Adding new test parameters

To add a new test parameter to be explored, edit py/params.py and subclass the appropriate *Param class and add the parameter to module variable PARAMETERS. Each parameter instance implements the modification to the test inputs (e.g. Kubernetes deployment yaml) necessary to set the value.

Building the dnsperf image

See image/README.md.