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pipeline is an all-batteries-included utility which consumes IOS XR telemetry streams directly from the router or indirectly from a publish/subscribe bus. Once collected, pipeline can perform some limited transformations of the data and forwards the resulting content on to a downstream, typically off-the-shelf, consumer. Supported downstream consumers include Apache Kafka, Influxdata TICK stack, prometheus, dedicated gRPC clients, as well as dump-to-file for diagnostics.

Other consumers (e.g. Elasticsearch, Splunk) can be setup to consume transformed telemetry data off the kafka bus.

Transformations performed by pipeline include producing JSON (from GPB/GPBKB inputs), template based transformation and metrics extraction (for TSDB consumption).

The binary for pipeline is included under

. This together with the configuration file
(only if you wish to export telemetry metrics to influxdb or prometheus) is all you need to collect telemetry from Cisco IOS XR and NXOS routers.

A simple script is provided to setup monitoring of pipeline should it be required. (See 'Monitoring the pipeline' below).

pipeline inputs

pipeline supports multiple different input transport modules: TCP, gRPC/HTTP2, UDP, Apache Kafka) carrying streaming telemetry. Multiple instances of any type of input module can be run in parallel.

pipeline supports running gRPC in both client and server mode on the input side. When running in server mode, routers running dialout Model Driven Telemetry (MDT) connect to pipeline and push content to pipeline. When running in client mode, pipeline connects to routers running MDT server side streaming (pipeline initiates connection to router). A TLS option is available in both cases.

TCP supports

wraps streaming telemetry payload (e.g. as described in Telemtry .proto message) and wraps it up in a simple streaming telemetry header. TCP input module can be setup with TCP Keepalive (configuration option:
) in order to probe and validate connections in case connection is idle beyond the keepalive period.

Compact GPB, GPB K/V and JSON payload can be carried over the transport inputs supported.

Do note that the ability to decode compact GPB is dependent on the subset of .protos compiled in via the

module in the vendor subtree. By default, a popular subset of models is included, but others can be pulled in and recompiled.

pipeline can also replay archives of streaming telemetry (operating in headless mode without a network). The archives would have been captured previously using the

output module configured with
option. For details about the replay module, look at the section 'Tap Module Type'.

pipeline outputs

Apache Kafka

On the output side, pipeline is capable of producing to multiple kafka broker sets, with each pipeline input stage automatically connected to each output stage. Depending on the input payload, it will be possible to request a matching output; e.g. for compact GPB input, we can publish JSON, JSON events or compact GPB messages. This output stage allows us to bring telemetry to most consumers which can consume off a kafka bus directly. Note that some transformation of the payload can happen within pipeline (e.g. take compact GPB in on the input side, produce JSON on the output side, take GPB K/V on the input side, produce text template transformed content on to kafka.)

Re-stream Over gRPC

pipeline can also operate an output stage as a gRPC server allowing down stream consumers clients to connect to pipeline, and benefit from server side streaming of (possibly transformed) content. The gRPC service specification is included here:


Metrics Extraction


output module allows pipeline to transform the telemetry messages received into metrics streams which can then be persisted to a TSDB like
and visualised using off-the-shelf tools like

Grafana snapshot

Another example shows memory utilisation per process:

Grafana snapshot

The graphs present streaming telemetry data from a router configured with the following paths:

sensor-group LOAD
  sensor-path Cisco-IOS-XR-wdsysmon-fd-oper:system-monitoring/cpu-utilization
  sensor-path Cisco-IOS-XR-nto-misc-oper:memory-summary/nodes/node/summary
  sensor-path Cisco-IOS-XR-procmem-oper:processes-memory/nodes/node/process-ids/process-id
  sensor-path Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters

The streams were collected by a running pipeline, and, in one case, pushed to

, and in the other, pushed to
(via a push gateway in order to retain timestamps). From there, the metrics were queried and visualised using grafana.

An example recipe used by pipeline output metrics module to specify which metrics to collect, and how to key them can be found here:

Example metrics.json

Note that both GPB and GPB K/V input can be processed by metrics extraction. The output produced by the two forms is currently slightly different, note that the recipe for GPB should be this:

Example metrics.json for compact GPB

The metrics output module can also be configured to dump metrics to a local file for inspection and troubleshooting support. To do this, simply uncomment out the

directive and specify the filename to dump to.
# For troubleshooting purposes, it is possible to
# dump the metrics to a local file, in order to understand what is
# being exported from persepective of *pipeline* metrics module.
# dump = metricsdump.txt

Sample dump output for

output looks like this:
Server:, wkid 0, writing 60 points in db: robot_alertdb
(prec: [ms], consistency: [], retention: [])
    Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters,EncodingPath=Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters,Producer=leaf428,interface-name=Null0 bytes-received=0,bytes-sent=0,carrier-transitions=0i,crc-errors=0i,input-drops=0i,input-errors=0i,input-ignored-packets=0i,input-queue-drops=0i,output-buffer-failures=0i,output-drops=0i,output-errors=0i,output-queue-drops=0i,packets-received=0,packets-sent=0 1481581596836000000
    Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters,EncodingPath=Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters,Producer=leaf428,interface-name=Bundle-Ether2 bytes-received=470058,bytes-sent=430384,carrier-transitions=0i,crc-errors=0i,input-drops=0i,input-errors=0i,input-ignored-packets=0i,input-queue-drops=0i,output-buffer-failures=0i,output-drops=0i,output-errors=0i,output-queue-drops=0i,packets-received=3709,packets-sent=3476 1481581596836000000
    Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters,EncodingPath=Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters,Producer=leaf428,interface-name=Bundle-Ether3 bytes-received=8270089146,bytes-sent=8270761380,carrier-transitions=0i,crc-errors=0i,input-drops=0i,input-errors=0i,input-ignored-packets=0i,input-queue-drops=0i,output-buffer-failures=0i,output-drops=0i,output-errors=0i,output-queue-drops=0i,packets-received=66694385,packets-sent=66702839 1481581596836000000

In this example consistency and retention policy are not specified and fall back to the default.

Sample dump output for

output looks like this:
packets_received{Basepath="Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters",Producer="",interface_name="HundredGigE0/0/1/3"} 244204 1470694071166
bytes_received{Basepath="Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters",Producer="",interface_name="HundredGigE0/0/1/3"} 16324149 1470694071166
packets_sent{Basepath="Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters",Producer="",interface_name="HundredGigE0/0/1/3"} 24 1470694071166
bytes_sent{Basepath="Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters",Producer="",interface_name="HundredGigE0/0/1/3"} 1680 1470694071166
output_drops{Basepath="Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters",Producer="",interface_name="HundredGigE0/0/1/3"} 0 1470694071166
carrier_transitions{Basepath="Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface/latest/generic-counters",Producer="",interface_name="HundredGigE0/0/1/3"} 0 1470694071166

The output module is implemented in a way which makes supporting other metrics consumers easy. For example adding support for druid or OpenTSDB would require implementing a simple adaptation interface.

A description of the configuration options is available in the annotated pipeline.conf. A sample configuration for an

setup might look like this:
stage = xport_output
type = metrics
file = metrics.json
output = influx
influx =
database = alertdb
workers = 10
datachanneldepth = 1000

When the Influx configuration is run for the first time, username/password credentials are requested. If a key pair is provided via the

option (run
pipeline -help
for details
), then an
alternative configuration file (with
) suffix is written
including the encrypted password. This
REWRITTEN` configuration can be used to avoid the interactive stage in subsequent pipeline runs.

An example setup for a

setup might look like this:
stage = xport_output
type = metrics
file = metrics.json
datachanneldepth = 1000
output = prometheus
pushgw =
jobname = telemetry
instance = pipelinemetrics

Tap Module Type

The 'tap' output module can be used to dump decoded content for troubleshooting purposes too. This module attempts to publish content in JSON, and falls back to a hex dump (e.g. if corresponding .proto package is not available).

Full support is provided for compact GPB decoding; as long as the

golang binding has been imported. A common set (approximately a quarter of all models) is automatically imported into pipeline.

Replaying Archive of Telemetry Streams


output module can also be used to capture raw data to file simply by replacing the
option with a
option. With a configuration like the following, all input streams would be captured and dumped into
stage = xport_output
type = tap
raw = true
file = dump.bin
datachanneldepth = 1000

The content can be subsequently replayed using the

input module. The
input module can replay the archive in a loop, or for a finite number of messages (using the
option). The inter message gap can be controlled using the
option. Below is a
configuration snippet which would replay 1000 messages using the archive in
with an intermessage delay of a 100us. The archive could contain fewer messages than requested, in which case replay loops back to the start of the archive (do note that timestamps will appear reordered in such a case). An unspecified delay or a delay of 0 usec, will result in no measurable intermessage delay. In order to loop continuously through the archive, replace firstn option with loop=true.:
stage = xport_input
type = replay
file = dump.bin
firstn = 1000
delayusec = 100000

Text Template Transformation

Finally, output to kafka, tap and the grpc server can be manipulated using a text template engine. Further documentation will be added to support run time content transformation in the field. In the meantime an example can be found here:

Example text template spec

The template language is documented here.


Passwords are not stored in the clear in

. Whenever a password is required in interaction with some other service (e.g. gRPC dialin, Influx user credentials etc), pipeline adopts a consistent approach whereby passwords are stored encrypted by a public key.

The production workflow assumes that

is setup, for the specific input or output module/s, with an encrypted password. When an encrypted password is configured (in the form
), pipeline runs in a non-interactive way and expects the
option to lead to the private key. This private key is used to decrypt the password ciphertext whenever the password is required.

The production workflows looks like this:

  • pipeline run with a pipeline.conf including username and encrypted passwords
  • pem file previously used to encrypt passwords passed in as a command line argument to pipeline.

The one-time workflow to generate the password ciphertext is as follows:

  • make sure no username/password credentials are configured in the configuration file for the input/output module/s you wish to generate the password ciphertext for.
  • run pipeline, passing in the -pem option (pem provides the public key available for enoding with). This will result in an interactive session where user is prompted for username and password. For convenience, the configuration file is rewritten as
    with the username and password ciphertext included.

This rewritten configuration can be used directly in subsequent non-interactive runs using an invocation such as;

 ./pipeline -pem=./id_rsa -config pipeline.conf_REWRITTEN


The input and ouput modules can be combined in different ways across a number of pipeline instances to accommodate a customer deployment. For example, in its simplest form, a pipeline can be deployed with MDT gRPC + GPB K/V as its input stage, and tap as its output stage if all that is required is textual inspection of a JSON representation of the streams.

Alternatively, the kafka output plugin may be setup alongside or instead of the tap output plugin to feed kafka and kafka consumers as shown here:

Single pipeline deployment

Yet another alternative is to have a primary pipeline consume MDT telemetry streams and push on to kafka, and a secondary instance of pipeline consuming from kafka, and, for example, feeding metrics straight into prometheus as shown in the figure below:

Multi pipeline deployment

The third and final example shows multiple pipeline instances, some consuming telemetry from routers, and others consuming from kafka. On the output side, some instances are feeding kafka, whereas others are feeding a TSDB, InfluxDB in this case. In all case, pipeline instances are being scraped for monitoring purposes using prometheus. Note how the example includes a case where a pipeline instance is collecting directly from the router and feeding InfluxDB directly. The example also includes, what is probably a more production friendly scenario with a pipeline instance collecting from routers and feeding kafka, and other pipeline instances collecting from kafka and feeding InfluxDB. This way, should other applications wish to consume telemetry streams too, they can simply subscribe to kafka.

Multi pipeline deployment II


CLI options allow pipeline to be started with extra debugging, and with configuration file and log file changed from the default of

gows/src/pipeline$./pipeline --help
Startup pipeline
pipeline, version 0.6.1

-config="pipeline.conf": Specify the path and name of the configuration file -debug=false: Dump debugging information to logfile (for event dump see 'logdata' in config file) -log="pipeline.log": Specify the path and name of log file


Startup pipeline
Load config from [pipeline.conf], logging in [pipeline.log]
Wait for ^C to shutdown

and stopping...

Interrupt, stopping gracefully

Stopping input 'myrouters' Stopping output 'mykafka' Done

Logging output is stuctured to support log analysis. If the

command line option is specified and empty i.e.
, then log information is pushed to stdout, as well as fluentd if configured.

In order to monitor statistics like the number of messages flowing through pipeline, errors, queue depths etc, refer to the section "Monitoring pipeline" below.


Configuration is currently provided at startup in the form of a configuration file. The configuration is built of named sections with each section representing an input or output stage of a specified type, with other per type attributes. Here is a self-explanatory configuration file with a single input and output section:

Annotated pipeline.conf

Any number of input and output sections are supported. Note that currently all inputs are connected to all outputs.

In future, some level of dynamic configuration may be supported providing programmatic control of pipeline.

Configuration Template With Environment Variables

At any point in the configuration file it is possible to embed template text of the form

. Any such text, embedded anywhere in the configuration file, will be translated at runtime to the environment variable content. If the variable is not set, configuration will fail, and complain accordingly.

Example: consider a setup with two input sections; one terminate gRPC and another TCP dialout:

stage = xport_input
type = tcp
encap = st
listen =
keepalive_seconds = 7200

[lab-uk] stage = xport_input type = grpc encap = gpbkv listen =

In order to provide the

parameters as environment variables, e.g. TCPENDPOINT and GRPCENDPOINT, the configuration would need to be set up as follows:

[lab-sjc] stage = xportinput type = tcp encap = st listen = :{{.Env "TCPENDPOINT"}}

[lab-uk] stage = xportinput type = grpc encap = gpbkv listen = {{.Env "GRPCENDPOINT"}}

Note that wherever the

appear, a translation would be attempted in a preprocessing stage prior to loading the configuration.

Configuration -

There are two disinct scenarios when using

as the
stage. While the router is always the party streaming content, the router can either be acting in server role or client role. The role determines whether pipeline needs to be setup as the complimentary server ('listen=' directive specifies socket to listen on) or client ('server=' specifies the router to connect to). When pipeline is the client and initiates the connection to the router, the encoding to request must be configured ('encoding=' directive). On the other hand, if the pipeline is in the role of server, then whatever encoding is pushed from the router will be consumed so no
directive is required. The unified GPB header is sufficient for pipeline to determine what codec needs to be applied.

In the case of

with encapsulation set to
, pipeline can handle both prerelease and unified header encapsulation i.e. there is no need to specify the type of encoding expected.

Input encoding to output encoding matrix table:

|input enc | metrics | json | json_events | gpb | template xform | |-----------|---------|------|-------------|-----|----------------| |gpbkv | yes | yes | yes | yes(k/v) | yes | |gpbcompact | yes | yes | yes | yes | no | |json | no | yes | no | no | no |

Templating transformation for gpbcompact is expected to be added shortly. Templating transformation works with the following output stages: kafka, gRPC server, tap.

The encodings json, json_events and gpb (compact and k/v) can be pushed through kafka output module, tap and gRPC server. Metrics encoding can currently be pushed to a time series database (influx and prometheus are supported today).

TCP, gRPC (dialin and dialout) and kafka input modules can handle all three input formats.

Monitoring pipeline

pipeline exports internal state for visualisation purposes. The address and port used by pipeline are controlled through the

global attribute in the default section of

The following is an example of a grafana dashboard composed and used to monitor the pipeline itself:

Grafana snapshot

A script
available under
can be invoked to run
containers. By default, these are configured to scrape a local instance of pipeline exporting to the default port
. Simply point your browser at that host on port
, and a collection of dashboards provide for monitoring of the pipeline. These dashboards are configurable.

If you are running a fleet of pipeline instances, simply modify

to include multiple targets in the static configuration section for
pipeline. Equally if pipeline is running on a remote host simply replace
with the appropriate remote host address.

The content in the

tree and a running
instance is all that is required to monitor pipeline.

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