carbon-c-relay
==============

Carbon-like graphite line mode relay.

This project aims to be a fast replacement of the original [Carbon
relay](http://graphite.readthedocs.org/en/1.0/carbon-daemons.html#carbon-relay-py)

The main reason to build a replacement is performance and
configurability.  Carbon is single threaded, and sending metrics to
multiple consistent-hash clusters requires chaining of relays.  This
project provides a multithreaded relay which can address multiple
targets and clusters for each and every metric based on pattern matches.

There are a couple more replacement projects out there we know of, which
are [carbon-relay-ng](https://github.com/graphite-ng/carbon-relay-ng) and [graphite-relay](https://github.com/markchadwick/graphite-relay
)

Compared to carbon-relay-ng, this project does provide carbon's
consistent-hash routing.  graphite-relay, which does this, however
doesn't do metric-based matches to direct the traffic, which this
project does as well.  To date, carbon-c-relay can do aggregations,
failover targets and more.

The relay is a simple program that reads its routing information from a
file.  The command line arguments allow to set the location for this
file, as well as the amount of dispatchers (worker threads) to use for
reading the data from incoming connections and passing them onto the
right destination(s).  The route file supports two main constructs:
clusters and matches.  The first define groups of hosts data metrics can
be sent to, the latter define which metrics should be sent to which
cluster.  Aggregation rules are seen as matches.

For every metric received by the relay, cleansing is performed.  The
following changes are performed before any match, aggregate or rewrite
rule sees the metric:

  - double dot elimination (necessary for correctly functioning
    consistent hash routing)
  - trailing/leading dot elimination
  - whitespace normalisation (this mostly affects output of the relay
    to other targets: metric, value and timestamp will be separated by
    a single space only, ever)
  - irregular char replacement with underscores (\_), currently
    irregular is defined as not being in [0-9a-zA-Z-_:#], but can be
    overridden on the command line.

The route file syntax is as follows:

```
# comments are allowed in any place and start with a hash (#)

cluster <name>
    <forward | any_of [useall] | failover | <carbon_ch | fnv1a_ch | jump_fnv1a_ch> [replication <count>]>
        <host[:port][=instance] [proto <udp | tcp>]> ...
    ;
cluster <name>
    file [ip]
        </path/to/file> ...
    ;
match
        <* | expression ...>
    send to <cluster ... | blackhole>
    [stop]
    ;
rewrite <expression>
    into <replacement>
    ;
aggregate
        <expression> ...
    every <interval> seconds
    expire after <expiration> seconds
    [timestamp at <start | middle | end> of bucket]
    compute <sum | count | max | min | average |
             median | percentile<%> | variance | stddev> write to
        <metric>
    [compute ...]
    [send to <cluster ...>]
    [stop]
    ;
send statistics to <cluster ...>
	[stop]
	;
```

Multiple clusters can be defined, and need not to be referenced by a
match rule.   All clusters point to one or more hosts, except the `file`
cluster which writes to files in the local filesystem.  `host` may be an
IPv4 or IPv6 address, or a hostname.  Since host is followed by an
optional `:` and port, for IPv6 addresses not to be interpreted wrongly,
either a port must be given, or the IPv6 address surrounded by brackets,
e.g. `[::1]`.  An optional `proto udp` or `proto tcp` may be added to
specify the use of UDP or TCP to connect to the remote server.  When
omitted this defaults to a TCP connection.

The `forward` and `file` clusters simply send everything they receive to
all defined members (host addresses or files).  The `any_of` cluster is
a small
variant of the `forward` cluster, but instead of sending to all defined
members, it sends each incoming metric to one of defined members.  This
is not much useful in itself, but since any of the members can receive
each metric, this means that when one of the members is unreachable, the
other members will receive all of the metrics.  This can be useful when
the cluster points to other relays.  The `any_of` router tries to send
the same metrics consistently to the same destination.  The `failover`
cluster is like the `any_of` cluster, but sticks to the order in which
servers are defined.  This is to implement a pure failover scenario
between servers.  The `carbon_ch` cluster sends the metrics to the
member that is responsible according to the consistent hash algorithm
(as used in the original carbon), or multiple members if replication is
set to more than 1.  The `fnv1a_ch` cluster is a identical in behaviour
to `carbon_ch`, but it uses a different hash technique (FNV1a) which is
faster but more importantly defined to get by a limitation of
`carbon_ch` to use both host and port from the members.  This is useful
when multiple targets live on the same host just separated by port.  The
instance that original carbon uses to get around this can be set by
appending it after the port, separated by an equals sign, e.g.
`127.0.0.1:2006=a` for instance `a`.  When using the `fnv1a_ch` cluster,
this instance overrides the hash key in use.  This allows for many
things, including masquerading old IP addresses, but mostly to make the
hash key location to become agnostic of the (physical) location of that
key.  For example, usage like
`10.0.0.1:2003=4d79d13554fa1301476c1f9fe968b0ac` would allow to change
port and/or ip address of the server that receives data for the instance
key.  Obviously, this way migration of data can be dealt with much more
conveniently.  The `jump_fnv1a_ch` cluster is also a consistent hash
cluster like the previous two, but it does not take the server
information into account at all.  Whether this is useful to you depends
on your scenario.  The jump hash has a much better balancing over the
servers defined in the cluster, at the expense of not being able to
remove any server but the last in order.  What this means is that this
hash is fine to use with ever growing clusters where older nodes are
also replaced at some point.  If you have a cluster where removal of old
nodes takes place often, the jump hash is not suitable for you.  Jump
hash works with servers in an ordered list without gaps.  To influence
the ordering, the instance given to the server will be used as sorting
key.  Without, the order will be as given in the file.  It is a good
practice to fix the order of the servers with instances such that it is
explicit what the right nodes for the jump hash are.

DNS hostnames are resolved to a single address, according to the preference
rules in [RFC 3484](https://www.ietf.org/rfc/rfc3484.txt).  The `any_of`
cluster has an explicit `useall` flag that enables a hostname to resolve to
multiple addresses.  Each address returned becomes a cluster destination.

Match rules are the way to direct incoming metrics to one or more
clusters.  Match rules are processed top to bottom as they are defined
in the file.  It is possible to define multiple matches in the same
rule.  Each match rule can send data to one or more clusters.  Since
match rules "fall through" unless the `stop` keyword is added,
carefully crafted match expression can be used to target
multiple clusters or aggregations.  This ability allows to replicate
metrics, as well as send certain metrics to alternative clusters with
careful ordering and usage of the `stop` keyword.  The special cluster
`blackhole` discards any metrics sent to it.  This can be useful for
weeding out unwanted metrics in certain cases.  Because throwing metrics
away is pointless if other matches would accept the same data, a match
with as destination the blackhole cluster, has an implicit `stop`.

Rewrite rules take a regular input to match incoming metrics, and
transform them into the desired new metric name.  In the replacement,
backreferences are allowed to match capture groups defined in the input
regular expression.  A match of `server\.(x|y|z)\.` allows to use e.g.
`role.\1.` in the substitution.  A few caveats apply to the current
implementation of rewrite rules.  First, their location in the config
file determines when the rewrite is performed.  The rewrite is done
in-place, as such a match rule before the rewrite would match the
original name, a match rule after the rewrite no longer matches the
original name.  Care should be taken with the ordering, as multiple
rewrite rules in succession can take place, e.g. `a` gets replaced by
`b` and `b` gets replaced by `c` in a succeeding rewrite rule.  The
second caveat with the current implementation, is that the rewritten
metric names are not cleansed, like newly incoming metrics are.  Thus,
double dots and potential dangerous characters can appear if the
replacement string is crafted to produce them.  It is the responsibility
of the writer to make sure the metrics are clean.  If this is an issue
for routing, one can consider to have a rewrite-only instance that
forwards all metrics to another instance that will do the routing.
Obviously the second instance will cleanse the metrics as they come in.
The backreference notation allows to lowercase and uppercase the
replacement string with the use of the underscore (`_`) and carret
(`^`) symbols following directly after the backslash.  For example,
`role.\_1.` as substitution will lowercase the contents of `\1`.

The aggregations defined take one or more input metrics expressed by one
or more regular expresions, similar to the match rules.  Incoming
metrics are aggregated over a period of time defined by the interval in
seconds.  Since events may arrive a bit later in time, the expiration
time in seconds defines when the aggregations should be considered
final, as no new entries are allowed to be added any more.  On top of an
aggregation multiple aggregations can be computed.  They can be of the
same or different aggregation types, but should write to a unique new
metric.  The metric names can include back references like in rewrite
expressions, allowing for powerful single aggregation rules that yield
in many aggregations.  When no `send to` clause is given, produced
metrics are sent to the relay as if they were submitted from the
outside, hence match and aggregation rules apply to those.  Care should
be taken that loops are avoided this way.  For this reason, the use of
the `send to` clause is encouraged, to direct the output traffic where
possible.  Like for match rules, it is possible to define multiple
cluster targets.  Also, like match rules, the `stop` keyword applies to
control the flow of metrics in the matching process.

The special `send statistics to` construct is much like a `match` rule
which matches the (internal) statistics produced by the relay.  It can
be used to avoid router loops when sending the statistics to a certain
destination.  The `send statistics` construct can only be used once, but
multiple destinations can be used then required.


Examples
--------
Carbon-c-relay evolved over time, growing features on demand as the tool
proved to be stable and fitting the job well.  Below follow some
annotated examples of constructs that can be used with the relay.

Clusters can be defined as much as necessary.  They receive data from
match rules, and their type defines which members of the cluster finally
get the metric data.  The simplest cluster form is a `forward` cluster:

    cluster send-through
        forward
            10.1.0.1
        ;

Any metric sent to the `send-through` cluster would simply be forwarded to
the server at IPv4 address `10.1.0.1`.  If we define multiple servers,
all of those servers would get the same metric, thus:

    cluster send-through
        forward
            10.1.0.1
            10.2.0.1
        ;

The above results in a duplication of metrics send to both machines.
This can be useful, but most of the time it is not.  The `any_of`
cluster type is like `forward`, but it sends each incoming metric to any
of the members.  The same example with such cluster would be:

    cluster send-to-any-one
        any_of 10.1.0.1:2010 10.1.0.1:2011;

This would implement a multipath scenario, where two servers are used,
the load between them is spread, but should any of them fail, all
metrics are sent to the remaining one.  This typically works well for
upstream relays, or for balancing carbon-cache processes running on the
same machine.  Should any member become unavailable, for instance due to
a rolling restart, the other members receive the traffic.  If it is
necessary to have true fail-over, where the secondary server is only
used if the first is down, the following would implement that:

    cluster try-first-then-second
        failover 10.1.0.1:2010 10.1.0.1:2011;

These types are different from the two consistent hash cluster types:

    cluster graphite
        carbon_ch
            127.0.0.1:2006=a
            127.0.0.1:2007=b
            127.0.0.1:2008=c
        ;

If a member in this example fails, all metrics that would go to that
member are kept in the queue, waiting for the member to return.  This
is useful for clusters of carbon-cache machines where it is desirable
that the same
metric ends up on the same server always.  The `carbon_ch` cluster type
is compatible with carbon-relay consistent hash, and can be used for
existing clusters populated by carbon-relay.  For new clusters, however,
it is better to use the `fnv1a_ch` cluster type, for it is faster, and
allows to balance over the same address but different ports without an
instance number, in constrast to `carbon_ch`.

Because we can use multiple clusters, we can also replicate without the
use of the `forward` cluster type, in a more intelligent way:

    cluster dc-old
        carbon_ch replication 2
            10.1.0.1
            10.1.0.2
            10.1.0.3
        ;
    cluster dc-new1
        fnv1a_ch replication 2
            10.2.0.1
            10.2.0.2
            10.2.0.3
        ;
    cluster dc-new2
        fnv1a_ch replication 2
            10.3.0.1
            10.3.0.2
            10.3.0.3
        ;

    match *
        send to dc-old
        ;
    match *
        send to
            dc-new1
            dc-new2
        stop
        ;

In this example all incoming metrics are first sent to `dc-old`, then
`dc-new1` and finally to `dc-new2`.  Note that the cluster type of
`dc-old` is different.  Each incoming metric will be send to 2 members
of all three clusters, thus replicating to in total 6 destinations.  For
each cluster the destination members are computed independently.
Failure of clusters or members does not affect the others, since all
have individual queues.  The above example could also be written using
three match rules for each dc, or one match rule for all three dcs.  The
difference is mainly in performance, the number of times the incoming
metric has to be matched against an expression.  The `stop` rule in
`dc-new` match rule is not strictly necessary in this example, because
there are no more following match rules.  However, if the match would
target a specific subset, e.g.  `^sys\.`, and more clusters would be
defined, this could be necessary, as for instance in the following
abbreviated example:

    cluster dc1-sys ... ;
    cluster dc2-sys ... ;

    cluster dc1-misc ... ;
    cluster dc2-misc ... ;

    match ^sys\. send to dc1-sys;
    match ^sys\. send to dc2-sys stop;

    match * send to dc1-misc;
    match * send to dc2-misc stop;

As can be seen, without the `stop` in dc2-sys' match rule, all metrics
starting with `sys.` would also be send to dc1-misc and dc2-misc.  It
can be that this is desired, of course, but in this example there is a
dedicated cluster for the `sys` metrics.

Suppose there would be some unwanted metric that unfortunately is
generated, let's assume some bad/old software.  We don't want to store
this metric.  The `blackhole` cluster is suitable for that, when it is
harder to actually whitelist all wanted metrics.  Consider the
following:

    match
            some_legacy1$
            some_legacy2$
        send to blackhole
        stop;

This would throw away all metrics that end with `some_legacy`, that
would otherwise be hard to filter out.  Since the order matters, it
can be used in a construct like this:

    cluster old ... ;
    cluster new ... ;

    match * send to old;

    match unwanted send to blackhole stop;

    match * send to new;

In this example the old cluster would receive the metric that's unwanted
for the new cluster.  So, the order in which the rules occur does
matter for the execution.

The relay is capable of rewriting incoming metrics on the fly.  This
process is done based on regular expressions with capture groups that
allow to substitute parts in a replacement string.  Rewrite rules allow
to cleanup metrics from applications, or provide a migration path.  In
it's simplest form a rewrite rule looks like this:

    rewrite ^server\.(.+)\.(.+)\.([a-zA-Z]+)([0-9]+)
        into server.\_1.\2.\3.\3\4
        ;

In this example a metric like `server.DC.role.name123` would be
transformed into `server.dc.role.name.name123`.
For rewrite rules hold the same as for matches, that their order
matters.  Hence to build on top of the old/new cluster example done
earlier, the following would store the original metric name in the old
cluster, and the new metric name in the new cluster:

    match * send to old;

    rewrite ... ;

    match * send to new;

Note that after the rewrite, the original metric name is no longer
available, as the rewrite happens in-place.

Aggregations are probably the most complex part of carbon-c-relay.  Two
ways of specifying aggregates are supported by carbon-c-relay.  The
first, static rules, are handled by an optimiser which tries to fold
thousands of rules into groups to make the matching more efficient.  The
second, dynamic rules, are very powerful compact definitions with
possibly thousands of internal instantiations.  A typical static
aggregation looks like:

    aggregate
            ^sys\.dc1\.somehost-[0-9]+\.somecluster\.mysql\.replication_delay
            ^sys\.dc2\.somehost-[0-9]+\.somecluster\.mysql\.replication_delay
        every 10 seconds
        expire after 35 seconds
        timestamp at end of bucket
        compute sum write to
            mysql.somecluster.total_replication_delay
        compute average write to
            mysql.somecluster.average_replication_delay
        compute max write to
            mysql.somecluster.max_replication_delay
        compute count write to
            mysql.somecluster.replication_delay_metric_count
        ;

In this example, four aggregations are produced from the incoming
matching metrics.  In this example we could have written the two matches
as one, but for demonstration purposes we did not.  Obviously they can
refer to different metrics, if that makes sense.  The `every 10 seconds`
clause specifies in what interval the aggregator can expect new metrics
to arrive.  This interval is used to produce the aggregations, thus each
10 seconds 4 new metrics are generated from the data received sofar.
Because data may be in transit for some reason, or generation stalled,
the `expire after` clause specifies how long the data should be kept
before considering a data bucket (which is aggregated) to be complete.
In the example, 35 was used, which means after 35 seconds the first
aggregates are produced.  It also means that metrics can arrive 35
seconds late, and still be taken into account.  The exact time at which
the aggregate metrics are produced is random between 0 and interval (10
in this case) seconds after the expiry time.  This is done to prevent
thundering herds of metrics for large aggregation sets.
The `timestamp` that is used for the aggregations can be specified to be
the `start`, `middle` or `end` of the bucket.  Original
carbon-aggregator.py uses `start`, while carbon-c-relay's default has
always been `end`.
The `compute` clauses demonstrate a single aggregation rule can produce
multiple aggregates, as often is the case.  Internally, this comes for
free, since all possible aggregates are always calculated, whether or
not they are used.  The produced new metrics are resubmitted to the
relay, hence matches defined before in the configuration can match
output of the aggregator.  It is important to avoid loops, that can be
generated this way.  In general, splitting aggregations to their own
carbon-c-relay instance, such that it is easy to forward the produced
metrics to another relay instance is a good practice.

The previous example could also be written as follows to be dynamic:

    aggregate
            ^sys\.dc[0-9].(somehost-[0-9]+)\.([^.]+)\.mysql\.replication_delay
        every 10 seconds
        expire after 35 seconds
        compute sum write to
            mysql.host.\1.replication_delay
        compute sum write to
            mysql.host.all.replication_delay
        compute sum write to
            mysql.cluster.\2.replication_delay
        compute sum write to
            mysql.cluster.all.replication_delay
        ;

Here a single match, results in four aggregations, each of a different
scope.  In this example aggregation based on hostname and cluster are
being made, as well as the more general `all` targets, which in this
example have both identical values.  Note that with this single
aggregation rule, both per-cluster, per-host and total aggregations are
produced.  Obviously, the input metrics define which hosts and clusters
are produced.

With use of the `send to` clause, aggregations can be made more
intuitive and less error-prone.  Consider the below example:

    cluster graphite fnv1a_ch ip1 ip2 ip3;

    aggregate ^sys\.somemetric
        every 60 seconds
        expire after 75 seconds
        compute sum write to
            sys.somemetric
        send to graphite
        stop
        ;

    match * send to graphite;

It sends all incoming metrics to the graphite cluster, except the
sys.somemetric ones, which it replaces with a sum of all the incoming
ones.  Without a `stop` in the aggregate, this causes a loop, and
without the `send to`, the metric name can't be kept its original name,
for the output now directly goes to the cluster.


Statistics
----------
When carbon-c-relay is run without `-d` or `-s` arguments, statistics
will be produced and sent to the relay itself in the form of
`carbon.relays.<hostname>.*`.  The hostname is determined on startup,
and can be overriden using the `-H` argument.  While many metrics have a
similar name to what carbon-cache.py would produce, their values are
different.  To obtain a more compatible set of values, the `-m` argument
can be used to make values non-cumulative, that is, they will report the
change compared to the previous value.  By default, most values are
running counters which only increase over time.  The use of the
nonNegativeDerivative() function from graphite is useful with these.
The default sending interval is 1 minute (60 seconds), but can be
overridden using the `-S` argument specified in seconds.

The following metrics are produced in the `carbon.relays.<hostname>`
namespace:

* metricsReceived
  
  The number of metrics that were received by the relay.  Received here
  means that they were seen and processed by any of the dispatchers.

* metricsSent

  The number of metrics that were sent from the relay.  This is a total
  count for all servers combined.  When incoming metrics are duplicated
  by the cluster configuration, this counter will include all those
  duplications.  In other words, the amount of metrics that were
  successfully sent to other systems.  Note that metrics that are
  processed (received) but still in the sending queue (queued) are not
  included in this counter.

* metricsQueued

  The total number of metrics that are currently in the queues for all
  the server targets.  This metric is not cumulative, for it is a sample
  of the queue size, which can (and should) go up and down.  Therefore
  you should not use the derivative function for this metric.

* metricsDropped

  The total number of metric that had to be dropped due to server queues
  overflowing.  A queue typically overflows when the server it tries to
  send its metrics to is not reachable, or too slow in ingesting the
  amount of metrics queued.  This can be network or resource related,
  and also greatly depends on the rate of metrics being sent to the
  particular server.

* metricsBlackholed

  The number of metrics that did not match any rule, or matched a rule
  with blackhole as target.  Depending on your configuration, a high
  value might be an indication of a misconfiguration somewhere.  These
  metrics were received by the relay, but never sent anywhere, thus they
  disappeared.

* metricStalls

  The number of times the relay had to stall a client to indicate that
  the downstream server cannot handle the stream of metrics.  A stall is
  only performed when the queue is full and the server is actually
  receptive of metrics, but just too slow at the moment.  Stalls
  typically happen during micro-bursts, where the client typically is
  unaware that it should stop sending more data, while it is able to.

* connections

  The number of connect requests handled.  This is an ever increasing
  number just counting how many connections were accepted.

* disconnects

  The number of disconnected clients.  A disconnect either happens
  because the client goes away, or due to an idle timeout in the relay.
  The difference between this metric and connections is the amount of
  connections actively held by the relay.  In normal situations this
  amount remains within reasonable bounds.  Many connections, but few
  disconnections typically indicate a possible connection leak in the
  client.  The idle connections disconnect in the relay here is to guard
  against resource drain in such scenarios.

* dispatch\_wallTime\_us

  The number of microseconds spent by the dispatchers to do their work.
  In particular on multi-core systems, this value can be confusing,
  however, it indicates how long the dispatchers were doing work
  handling clients.  It includes everything they do, from reading data
  from a socket, cleaning up the input metric, to adding the metric to
  the appropriate queues.  The larger the configuration, and more
  complex in terms of matches, the more time the dispatchers will spend
  on the cpu.  But also time they do /not/ spend on the cpu is included
  in this number.  It is the pure wallclock time the dispatcher was
  serving a client.

* dispatch\_sleepTime\_us

  The number of microseconds spent by the dispatchers sleeping waiting
  for work.  When this value gets small (or even zero) the dispatcher
  has so much work that it doesn't sleep any more, and likely can't
  process the work in a timely fashion any more.  This value plus the
  wallTime from above sort of sums up to the total uptime taken by this
  dispatcher.  Therefore, expressing the wallTime as percentage of this
  sum gives the busyness percentage draining all the way up to 100% if
  sleepTime goes to 0.

* server\_wallTime\_us

  The number of microseconds spent by the servers to send the metrics
  from their queues.  This value includes connection creation, reading
  from the queue, and sending metrics over the network.

* dispatcherX

  For each indivual dispatcher, the metrics received and blackholed plus
  the wall clock time.  The values are as described above.

* destinations.X

  For all known destinations, the number of dropped, queued and sent
  metrics plus the wall clock time spent.  The values are as described
  above.

* aggregators.metricsReceived

  The number of metrics that were matched an aggregator rule and were
  accepted by the aggregator.  When a metric matches multiple
  aggregators, this value will reflect that.  A metric is not counted
  when it is considered syntactically invalid, e.g. no value was found.

* aggregators.metricsDropped

  The number of metrics that were sent to an aggregator, but did not fit
  timewise.  This is either because the metric was too far in the past
  or future.  The expire after clause in aggregate statements controls
  how long in the past metric values are accepted.

* aggregators.metricsSent

  The number of metrics that were sent from the aggregators.  These
  metrics were produced and are the actual results of aggregations.


Performance
-----------
The original argument for building carbon-c-relay was speed, with
configurablility following close.  To date, performance has bypassed the
original carbon-relay.py by orders of magnitude, but the actual speed
highly depends on perception and scenario.  What follows below are some
rough numbers about the environment at Booking.com where carbon-c-relay
is used extensively in production.

carbon-c-relay runs on all of our machines as a local submission relay.
Its config is simply a match all to a `any_of` cluster with a number of
upstream relays to try and send the metrics to.  These relays run with 4
workers, and receive a minimal amount of metrics per minute, typically
between 50 and 200.  These instances take typically around 19MiB of RAM
and consume at top 0.8% CPU of a 2.4GHz core.  The minimal footprint of
the relay is a desired property for running on all of our machines.

The main relays we run, have roughly 20 clusters defined with `fnv1a_ch`
hash.  Average clustersize around 10 members.  On top of that 30 match
rules are defined.  For a mildly-loaded relay receiving 1M metrics per
minute, the relay consumes 750MiB of RAM and needs around 40% of a
2.4GHz core.  A relay with more load but the same configuration, 3M
metrics per minute, needs almost 2GiB of RAM, and some 45% CPU of a
2.4GHz core.  The memory usage is mainly in the buffers for writing to
the server stores.

On the stores, we run relays with a simple config with a match all rule
to an `any_of` cluster pointing to 13 locally running carbon-cache.py
instances.  These relays receive up to 1.7M metrics per minute, and
require some 110MiB RAM for that.  The CPU usage is around 15% of a
2.4GHz core.

For aggregations we don't do much traffic (55K per minute) on a couple
of aggregations expanding to a thousand of metrics.  In our setup this
takes 30MiB of RAM usage with some 30% CPU usage.


Author
------
Fabian Groffen


Acknowledgement
---------------
This program was originally developed for Booking.com.  With approval
from Booking.com, the code was generalised and published as Open Source
on github, for which the author would like to express his gratitude.