- Modified metric expression and legend format in State of distribution links
- Changed panel type from 'flant-statusmap-panel' to 'status-history' for Process state
- Updated metric expressions to include instance filtering with {instance=\"$node\"}
for the following metrics:
- erlang_vm_statistics_run_queues_length
- erlang_vm_statistics_dirty_io_run_queue_length
- erlang_vm_statistics_dirty_cpu_run_queue_length
- Added 'DS_PROMETHEUS' as a templated data source variable
`init_per_group/3`, which starts the broker, was already called earlier
in the function.
This fixes a bug where the node can't be stopped in `end_per_group/2`,
attecting the next group ability to start one.
* Add BEAM dashboard
Also update the other dashboards by opening in Grafana v11.2.2 and ensuring they work as expected.
* Update the Erlang-Distributions-Compare dashboard
* Update the RabbitMQ-Overview dashboard
* Update the RabbitMQ-Quorum-Queues-Raft dashboard
* Update the RabbitMQ-Stream dashboard
* Update distribution link status panel
---------
Co-authored-by: Michal Kuratczyk <mkuratczyk@vmware.com>
* Add global histogram metrics for received message sizes per-protocol
fixup: add new files to bazel
fixup: expose message_size_bytes as prometheus classic histogram type
`rabbit_msg_size_metrics` does not use `seshat` any more, but
`counters` directly.
fixup: add msg_size_metrics unit test
* Improve message size histogram
1.
Avoid unnecessary time series emitted for stream protocol
The stream protocol cannot observe message sizes.
This commit ensures that the following time series are omitted:
```
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="64"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="256"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="1024"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="4096"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="16384"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="65536"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="262144"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="1048576"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="4194304"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="16777216"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="67108864"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="268435456"} 0
rabbitmq_global_message_size_bytes_bucket{protocol="stream",le="+Inf"} 0
rabbitmq_global_message_size_bytes_count{protocol="stream"} 0
rabbitmq_global_message_size_bytes_sum{protocol="stream"} 0
```
This reduces the number of time series by 15.
2.
Further reduce the number of time series by reducing the number of
buckets. Instead of 13 bucktes, emit only 9 buckets. Buckets are not
free, each is an extra time series stored.
Prior to this commit:
```
curl -s -u guest:guest localhost:15692/metrics | ag message_size | wc -l
92
```
After this commit:
```
curl -s -u guest:guest localhost:15692/metrics | ag message_size | wc -l
57
```
3.
The emitted metric should be called
`rabbitmq_message_size_bytes_bucket` instead of `rabbitmq_global_message_size_bytes_bucket`.
The latter is poor naming. There is no need to use `global` in
the metric name given that this metric doesn't exist in the old flawed
aggregated metrics.
4.
This commit simplies module `rabbit_global_counters`.
5.
Avoid garbage collecting the 10-elements list of buckets per message
being received.
---------
Co-authored-by: Péter Gömöri <peter@84codes.com>
Adds a specific clause on the
`prometheus_rabbitmq_core_metrics_collector:labels` function when the
associated metric item is a Queue + Exchange combo (`{Queue, Exchange}`)
By default Ra will use the cluster name as the metrics key. Currently
atom values are ignored by the prometheus plugin's tag rendering
functions, so if you have a QQ and Khepri running and request the
`/metrics/per-object` or `/metrics/detailed` endpoints you'll see values
that don't have labels set for the `ra_metrics` metrics:
# TYPE rabbitmq_raft_term_total counter
# HELP rabbitmq_raft_term_total Current Raft term number
rabbitmq_raft_term_total{vhost="/",queue="qq"} 9
rabbitmq_raft_term_total 10
With this change we map the name of the Ra cluster to a "raft_cluster"
tag, so instead an example metric might be:
# TYPE rabbitmq_raft_term_total counter
# HELP rabbitmq_raft_term_total Current Raft term number
rabbitmq_raft_term_total{vhost="/",queue="qq"} 9
rabbitmq_raft_term_total{raft_cluster="rabbitmq_metadata"} 10
This affects metrics for Khepri and the stream coordinator.
Collecting them on a large system (tens of thousands of processes
or more) can be time consuming as we iterate over all processes.
By putting them on a separate endpoint, we make that opt-in
Add copies of some per-object metrics that are labeled per-channel
aggregated to reduce cardinality. These metrics are valuable and
easier to process if exposed on per-exchange and per-queue basis.
We don't need to duplicate so many patterns in so many
files since we have a monorepo (and want to keep it).
If I managed to miss something or remove something that
should stay, please put it back. Note that monorepo-wide
patterns should go in the top-level .gitignore file.
Other .gitignore files are for application or folder-
specific patterns.
Part of the removal of file_handle_cache.
The Prometheus endpoint was updated but the Grafana dashboard
was not.
The FD stats are using the system's state rather than
file_handle_cache so there's no need to remove them.
* Fix broken dashboards if detailed metrics are used
If detailed metrics are pulled into the same prometheus, then
we get an error in Grafana:
execution: many-to-many matching not allowed:
matching labels must be unique on one side
This is because both endpoints provide `rabbit_identity_info`
which is not unique to the endpoint.
* add detailed metric scraper to prometheus config
---------
Co-authored-by: Michal Kuratczyk <michal.kuratczyk@broadcom.com>
For example special characters like double quotes are allowed in queue
names, in which case detailed metrics could produce unparsable text
format output.
[Why]
Mnesia is a very powerful and convenient tool for Erlang applications:
it is a persistent disc-based database, it handles replication accross
multiple Erlang nodes and it is available out-of-the-box from the
Erlang/OTP distribution. RabbitMQ relies on Mnesia to manage all its
metadata:
* virtual hosts' properties
* intenal users
* queue, exchange and binding declarations (not queues data)
* runtime parameters and policies
* ...
Unfortunately Mnesia makes it difficult to handle network partition and,
as a consequence, the merge conflicts between Erlang nodes once the
network partition is resolved. RabbitMQ provides several partition
handling strategies but they are not bullet-proof. Users still hit
situations where it is a pain to repair a cluster following a network
partition.
[How]
@kjnilsson created Ra [1], a Raft consensus library that RabbitMQ
already uses successfully to implement quorum queues and streams for
instance. Those queues do not suffer from network partitions.
We created Khepri [2], a new persistent and replicated database engine
based on Ra and we want to use it in place of Mnesia in RabbitMQ to
solve the problems with network partitions.
This patch integrates Khepri as an experimental feature. When enabled,
RabbitMQ will store all its metadata in Khepri instead of Mnesia.
This change comes with behavior changes. While Khepri remains disabled,
you should see no changes to the behavior of RabbitMQ. If there are
changes, it is a bug. After Khepri is enabled, there are significant
changes of behavior that you should be aware of.
Because it is based on the Raft consensus algorithm, when there is a
network partition, only the cluster members that are in the partition
with at least `(Number of nodes in the cluster ÷ 2) + 1` number of nodes
can "make progress". In other words, only those nodes may write to the
Khepri database and read from the database and expect a consistent
result.
For instance in a cluster of 5 RabbitMQ nodes:
* If there are two partitions, one with 3 nodes, one with 2 nodes, only
the group of 3 nodes will be able to write to the database.
* If there are three partitions, two with 2 nodes, one with 1 node, none
of the group can write to the database.
Because the Khepri database will be used for all kind of metadata, it
means that RabbitMQ nodes that can't write to the database will be
unable to perform some operations. A list of operations and what to
expect is documented in the associated pull request and the RabbitMQ
website.
This requirement from Raft also affects the startup of RabbitMQ nodes in
a cluster. Indeed, at least a quorum number of nodes must be started at
once to allow nodes to become ready.
To enable Khepri, you need to enable the `khepri_db` feature flag:
rabbitmqctl enable_feature_flag khepri_db
When the `khepri_db` feature flag is enabled, the migration code
performs the following two tasks:
1. It synchronizes the Khepri cluster membership from the Mnesia
cluster. It uses `mnesia_to_khepri:sync_cluster_membership/1` from
the `khepri_mnesia_migration` application [3].
2. It copies data from relevant Mnesia tables to Khepri, doing some
conversion if necessary on the way. Again, it uses
`mnesia_to_khepri:copy_tables/4` from `khepri_mnesia_migration` to do
it.
This can be performed on a running standalone RabbitMQ node or cluster.
Data will be migrated from Mnesia to Khepri without any service
interruption. Note that during the migration, the performance may
decrease and the memory footprint may go up.
Because this feature flag is considered experimental, it is not enabled
by default even on a brand new RabbitMQ deployment.
More about the implementation details below:
In the past months, all accesses to Mnesia were isolated in a collection
of `rabbit_db*` modules. This is where the integration of Khepri mostly
takes place: we use a function called `rabbit_khepri:handle_fallback/1`
which selects the database and perform the query or the transaction.
Here is an example from `rabbit_db_vhost`:
* Up until RabbitMQ 3.12.x:
get(VHostName) when is_binary(VHostName) ->
get_in_mnesia(VHostName).
* Starting with RabbitMQ 3.13.0:
get(VHostName) when is_binary(VHostName) ->
rabbit_khepri:handle_fallback(
#{mnesia => fun() -> get_in_mnesia(VHostName) end,
khepri => fun() -> get_in_khepri(VHostName) end}).
This `rabbit_khepri:handle_fallback/1` function relies on two things:
1. the fact that the `khepri_db` feature flag is enabled, in which case
it always executes the Khepri-based variant.
4. the ability or not to read and write to Mnesia tables otherwise.
Before the feature flag is enabled, or during the migration, the
function will try to execute the Mnesia-based variant. If it succeeds,
then it returns the result. If it fails because one or more Mnesia
tables can't be used, it restarts from scratch: it means the feature
flag is being enabled and depending on the outcome, either the
Mnesia-based variant will succeed (the feature flag couldn't be enabled)
or the feature flag will be marked as enabled and it will call the
Khepri-based variant. The meat of this function really lives in the
`khepri_mnesia_migration` application [3] and
`rabbit_khepri:handle_fallback/1` is a wrapper on top of it that knows
about the feature flag.
However, some calls to the database do not depend on the existence of
Mnesia tables, such as functions where we need to learn about the
members of a cluster. For those, we can't rely on exceptions from
Mnesia. Therefore, we just look at the state of the feature flag to
determine which database to use. There are two situations though:
* Sometimes, we need the feature flag state query to block because the
function interested in it can't return a valid answer during the
migration. Here is an example:
case rabbit_khepri:is_enabled(RemoteNode) of
true -> can_join_using_khepri(RemoteNode);
false -> can_join_using_mnesia(RemoteNode)
end
* Sometimes, we need the feature flag state query to NOT block (for
instance because it would cause a deadlock). Here is an example:
case rabbit_khepri:get_feature_state() of
enabled -> members_using_khepri();
_ -> members_using_mnesia()
end
Direct accesses to Mnesia still exists. They are limited to code that is
specific to Mnesia such as classic queue mirroring or network partitions
handling strategies.
Now, to discover the Mnesia tables to migrate and how to migrate them,
we use an Erlang module attribute called
`rabbit_mnesia_tables_to_khepri_db` which indicates a list of Mnesia
tables and an associated converter module. Here is an example in the
`rabbitmq_recent_history_exchange` plugin:
-rabbit_mnesia_tables_to_khepri_db(
[{?RH_TABLE, rabbit_db_rh_exchange_m2k_converter}]).
The converter module — `rabbit_db_rh_exchange_m2k_converter` in this
example — is is fact a "sub" converter module called but
`rabbit_db_m2k_converter`. See the documentation of a `mnesia_to_khepri`
converter module to learn more about these modules.
[1] https://github.com/rabbitmq/ra
[2] https://github.com/rabbitmq/khepri
[3] https://github.com/rabbitmq/khepri_mnesia_migration
See #7206.
Co-authored-by: Jean-Sébastien Pédron <jean-sebastien@rabbitmq.com>
Co-authored-by: Diana Parra Corbacho <dparracorbac@vmware.com>
Co-authored-by: Michael Davis <mcarsondavis@gmail.com>
Why:
A RabbitMQ operator should be able to see whether RabbitMQ drops MQTT
QoS 0 messages due to overload protection. It's an indication that an
MQTT subscriber does not consume fast enough.
How:
Use Prometheus global counters.
There are 2 valid solutions:
1. Introduce a new metric called messages_dropped specifically for the
rabbitmq_mqtt_qos0_queue type. This would work in a similar fashion
how streams extends the per protocol global counters, but requires
extending the per protocol & queue type global counters for the MQTT
QoS queue type. The emitted metrics would look as follows:
```
rabbitmq_global_messages_dropped_total{protocol="mqtt310",queue_type="rabbit_mqtt_qos0_queue"} 0
rabbitmq_global_messages_dropped_total{protocol="mqtt311",queue_type="rabbit_mqtt_qos0_queue"} 0
rabbitmq_global_messages_dropped_total{protocol="mqtt50",queue_type="rabbit_mqtt_qos0_queue"} 0
```
2. Reuse the existing metric rabbitmq_global_messages_dead_lettered_maxlen_total
This commit decides to go for the 2nd approach because:
a) there is no need to add a new metric. Even though dead lettering is not supported
for the MQTT QoS 0 queue type, this metric maps nicely to
what happens: The queue drop messages since itx max length
(mqtt.mailbox_soft_limit) is exceeded with overflow behaviour
drop-head. Furtheremore the label `dead_letter_strategy="disabled"` tells
that dead lettering is not taking place from this queue type.
b) this metric allows to support dead lettering for the MQTT QoS 0 queue
type in the future.
The new dead lettering metrics look as follows:
```
rabbitmq_global_messages_dead_lettered_maxlen_total{queue_type="rabbit_classic_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_maxlen_total{queue_type="rabbit_classic_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_maxlen_total{queue_type="rabbit_mqtt_qos0_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_maxlen_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_maxlen_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_expired_total{queue_type="rabbit_classic_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_expired_total{queue_type="rabbit_classic_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_expired_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_least_once"} 0
rabbitmq_global_messages_dead_lettered_expired_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_expired_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_rejected_total{queue_type="rabbit_classic_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_rejected_total{queue_type="rabbit_classic_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_rejected_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_least_once"} 0
rabbitmq_global_messages_dead_lettered_rejected_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_rejected_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_delivery_limit_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_least_once"} 0
rabbitmq_global_messages_dead_lettered_delivery_limit_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_most_once"} 0
rabbitmq_global_messages_dead_lettered_delivery_limit_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="disabled"} 0
rabbitmq_global_messages_dead_lettered_confirmed_total{queue_type="rabbit_quorum_queue",dead_letter_strategy="at_least_once"} 0
```
Péter Gömöri
markus812498
Anh Nguyen
Jean-Sébastien Pédron
Luke Bakken
Michael Klishin
David Ansari
Lois Soto Lopez
Michael Davis
Michal Kuratczyk
Simon Unge
Michael Klishin
Lois Soto Lopez
Loïc Hoguin
Michal Kuratczyk
Rin Kuryloski
Iliia Khaprov
Michael Klishin
Lajos Gerecs
Johan Rhodin
Diana Parra Corbacho