Statistics collector¶

Module stats gathers various counters from the query resolution and server internals, and offers them as a key-value storage. These metrics can be either exported to Graphite/InfluxDB/Metronome, exposed as Prometheus metrics endpoint, or processed using user-provided script as described in chapter Asynchronous events.

Note

Please remember that each Knot Resolver instance keeps its own statistics, and instances can be started and stopped dynamically. This might affect your data postprocessing procedures if you are using Multiple instances.

Built-in statistics¶

Built-in counters keep track of number of queries and answers matching specific criteria.

Global request counters
request.total	total number of DNS requests (including internal client requests)
request.internal	internal requests generated by Knot Resolver (e.g. DNSSEC trust anchor updates)
request.udp	external requests received over plain UDP (RFC 1035)
request.tcp	external requests received over plain TCP (RFC 1035)
request.dot	external requests received over DNS-over-TLS (RFC 7858)
request.doh	external requests received over DNS-over-HTTP (RFC 8484)
request.xdp	external requests received over plain UDP via an AF_XDP socket

Global answer counters
answer.total	total number of answered queries
answer.cached	queries answered from cache
answer.stale	queries that utilized stale data

Answers categorized by RCODE
answer.noerror	NOERROR answers
answer.nodata	NOERROR, but empty answers
answer.nxdomain	NXDOMAIN answers
answer.servfail	SERVFAIL answers

Answer latency
answer.1ms	completed in 1ms
answer.10ms	completed in 10ms
answer.50ms	completed in 50ms
answer.100ms	completed in 100ms
answer.250ms	completed in 250ms
answer.500ms	completed in 500ms
answer.1000ms	completed in 1000ms
answer.1500ms	completed in 1500ms
answer.slow	completed in more than 1500ms
answer.sum_ms	sum of all latencies in ms

Answer flags
answer.aa	authoritative answer
answer.tc	truncated answer
answer.ra	recursion available
answer.rd	recursion desired (in answer!)
answer.ad	authentic data (DNSSEC)
answer.cd	checking disabled (DNSSEC)
answer.do	DNSSEC answer OK
answer.edns0	EDNS0 present

Query flags
query.edns	queries with EDNS present
query.dnssec	queries with DNSSEC DO=1

Example:

modules.load('stats')

-- Enumerate metrics
> stats.list()
[answer.cached] => 486178
[iterator.tcp] => 490
[answer.noerror] => 507367
[answer.total] => 618631
[iterator.udp] => 102408
[query.concurrent] => 149

-- Query metrics by prefix
> stats.list('iter')
[iterator.udp] => 105104
[iterator.tcp] => 490

-- Fetch most common queries
> stats.frequent()
[1] => {
        [type] => 2
        [count] => 4
        [name] => cz.
}

-- Fetch most common queries (sorted by frequency)
> table.sort(stats.frequent(), function (a, b) return a.count > b.count end)

-- Show recently contacted authoritative servers
> stats.upstreams()
[2a01:618:404::1] => {
    [1] => 26 -- RTT
}
[128.241.220.33] => {
    [1] => 31 - RTT
}

-- Set custom metrics from modules
> stats['filter.match'] = 5
> stats['filter.match']
5

Module reference¶

stats.get(key)¶

Parameters:: key (string) – i.e. "answer.total"
Returns:: number

Return nominal value of given metric.

stats.set('key val')¶

Set nominal value of given metric.

Example:

stats.set('answer.total 5')
-- or syntactic sugar
stats['answer.total'] = 5

stats.list([prefix])¶

Parameters:: prefix (string) – optional metric prefix, i.e. "answer" shows only metrics beginning with “answer”

Outputs collected metrics as a JSON dictionary.

stats.upstreams()¶

Outputs a list of recent upstreams and their RTT. It is sorted by time and stored in a ring buffer of a fixed size. This means it’s not aggregated and readable by multiple consumers, but also that you may lose entries if you don’t read quickly enough. The default ring size is 512 entries, and may be overridden on compile time by -DUPSTREAMS_COUNT=X.

stats.frequent()¶

Outputs list of most frequent iterative queries as a JSON array. The queries are sampled probabilistically, and include subrequests. The list maximum size is 5000 entries, make diffs if you want to track it over time.

stats.clear_frequent()¶

Clear the list of most frequent iterative queries.

Graphite/InfluxDB/Metronome¶

The graphite sends statistics over the Graphite protocol to either Graphite, Metronome, InfluxDB or any compatible storage. This allows powerful visualization over metrics collected by Knot Resolver.

Tip

The Graphite server is challenging to get up and running, InfluxDB combined with Grafana are much easier, and provide richer set of options and available front-ends. Metronome by PowerDNS alternatively provides a mini-graphite server for much simpler setups.

Example configuration:

Only the host parameter is mandatory.

By default the module uses UDP so it doesn’t guarantee the delivery, set tcp = true to enable Graphite over TCP. If the TCP consumer goes down or the connection with Graphite is lost, resolver will periodically attempt to reconnect with it.

modules = {
        graphite = {
                prefix = hostname() .. worker.id, -- optional metric prefix
                host = '127.0.0.1',  -- graphite server address
                port = 2003,         -- graphite server port
                interval = 5 * sec,  -- publish interval
                tcp = false          -- set to true if you want TCP mode
        }
}

The module supports sending data to multiple servers at once.

modules = {
        graphite = {
                host = { '127.0.0.1', '1.2.3.4', '::1' },
        }
}

Dependencies¶

lua cqueues package.

Prometheus metrics endpoint¶

The HTTP module exposes /metrics endpoint that serves metrics from Statistics collector in Prometheus text format. You can use it as soon as HTTP module is configured:

$ curl -k https://localhost:8453/metrics | tail
# TYPE latency histogram
latency_bucket{le=10} 2.000000
latency_bucket{le=50} 2.000000
latency_bucket{le=100} 2.000000
latency_bucket{le=250} 2.000000
latency_bucket{le=500} 2.000000
latency_bucket{le=1000} 2.000000
latency_bucket{le=1500} 2.000000
latency_bucket{le=+Inf} 2.000000
latency_count 2.000000
latency_sum 11.000000

You can namespace the metrics in configuration, using http.prometheus.namespace attribute:

modules.load('http')
-- Set Prometheus namespace
http.prometheus.namespace = 'resolver_'

You can also add custom metrics or rewrite existing metrics before they are returned to Prometheus client.

modules.load('http')
-- Add an arbitrary metric to Prometheus
http.prometheus.finalize = function (metrics)
        table.insert(metrics, 'build_info{version="1.2.3"} 1')
end