Legacy Active Anti-Entropy
Riak’s active anti-entropy (AAE) subsystem is a set of background processes that repair object inconsistencies stemming from missing or divergent object values across nodes. Riak operators can turn AAE on and off and configure and monitor its functioning.
In Riak versions 2.9.1 and later, TicTac AAE is included with releases as an option to be used in addition to or instead of traditional AAE in Riak.
Enabling Active Anti-Entropy
Whether AAE is currently enabled in a node is determined by the value of
the anti_entropy
parameter in the node’s configuration files.
In Riak versions 2.0 and later, AAE is turned on by default.
anti_entropy = active
{riak_kv, [
{anti_entropy, {on, []}},
%% More riak_kv settings...
]}
For monitoring purposes, you can also activate AAE debugging, which provides verbose debugging message output:
anti_entropy = active-debug
{riak_kv, [
%% With debugging
{anti_entropy, {on, [debug]}},
%% More riak_kv settings...
]}
Remember that you will need to restart the node for any configuration-related changes to take effect.
Disabling Active Anti-Entropy
Alternatively, AAE can be switched off if you would like to repair object inconsistencies using read repair alone:
anti_entropy = passive
{riak_kv, [
%% AAE turned off
{anti_entropy, {off, []}},
%% More riak_kv settings...
]}
If you would like to reclaim the disk space used by AAE operations, you must manually delete the directory in which AAE-related data is stored in each node.
rm -Rf <path_to_riak_node>/data/anti_entropy/*
The default directory for AAE data is ./data/anti_entropy
, as in the
example above, but this can be changed. See the section below titled
Data Directory.
Remember that you will need to restart the node for any configuration-related changes to take effect.
The directory deletion method above can also be used to force a rebuilding of hash trees.
Monitoring AAE
Riak’s command-line interface includes a command that provides insight into AAE-related processes and performance:
riak admin aae-status
When you run this command in a node, the output will look like this (shortened for the sake of brevity):
================================== Exchanges ==================================
Index Last (ago) All (ago)
-------------------------------------------------------------------------------
0 19.0 min 20.3 min
22835963083295358096932575511191922182123945984 18.0 min 20.3 min
45671926166590716193865151022383844364247891968 17.3 min 19.8 min
68507889249886074290797726533575766546371837952 16.5 min 18.3 min
91343852333181432387730302044767688728495783936 15.8 min 17.3 min
...
================================ Entropy Trees ================================
Index Built (ago)
-------------------------------------------------------------------------------
0 5.7 d
22835963083295358096932575511191922182123945984 5.6 d
45671926166590716193865151022383844364247891968 5.5 d
68507889249886074290797726533575766546371837952 4.3 d
91343852333181432387730302044767688728495783936 4.8 d
================================ Keys Repaired ================================
Index Last Mean Max
-------------------------------------------------------------------------------
0 0 0 0
22835963083295358096932575511191922182123945984 0 0 0
45671926166590716193865151022383844364247891968 0 0 0
68507889249886074290797726533575766546371837952 0 0 0
91343852333181432387730302044767688728495783936 0 0 0
Each of these three tables contains information for each vnode in your cluster in these three categories:
Category | Measures | Description |
---|---|---|
Exchanges | Last |
When the most recent exchange between a data partition and one of its replicas was performed |
All |
How long it has been since a partition exchanged with all of its replicas | |
Entropy Trees | Built |
When the hash trees for a given partition were created |
Keys Repaired | Last |
The number of keys repaired during all key exchanges since the last node restart |
Mean |
The mean number of keys repaired during all key exchanges since the last node restart | |
Max |
The maximum number of keys repaired during all key exchanges since the last node restart |
All AAE status information obtainable using the riak admin aae-status
command is stored in-memory and is reset when a node is restarted with
the exception of hash tree build information, which is persisted on disk
(because hash trees themselves are persisted on disk).
Configuring AAE
Riak’s configuration files enable you not just to turn AAE on and off but also to fine-tune your cluster’s use of AAE, e.g. how much memory AAE processes should consume, how frequently specific processes should be run, etc.
Data Directory
By default, data related to AAE operations is stored in the
./data/anti_entropy
directory in each Riak node. This can be changed
by setting the anti_entropy.data_dir
parameter to a different value.
Bloom Filters
Bloom filters are mechanisms used to prevent reads that are destined to
fail because no object exists in the location that they’re querying.
Using bloom filters can improve reaction time for some queries, but
entail a small general performance cost. You can switch bloom filters
on and off using the anti_entropy.bloomfilter
parameter.
Trigger Interval
The anti_entropy.trigger_interval
setting determines how often Riak’s
AAE subsystem looks for work to do, e.g. building or expiring hash
trees, triggering information exchanges between nodes, etc. The default
is every 15 seconds (15s
). Raising this value may save resources, but
at a slightly higher risk of data corruption.
Hash Trees
As a fallback measure in addition to the normal operation of AAE on-disk
hash trees, Riak periodically clears and regenerates all hash trees
stored on disk to ensure that hash trees correspond to the key/value
data stored in Riak. This enables Riak to detect silent data corruption
resulting from disk failure or faulty hardware. The
anti_entropy.tree.expiry
setting enables you to determine how often
that takes place. The default is once a week (1w
). You can set up this
process to run once a day (1d
), twice a day (12h
), once a month
(4w
), and so on.
In addition to specifying how often Riak expires hash trees after they
are built, you can also specify how quickly and how many hash trees are
built. You can set the frequency using the
anti_entropy.tree.build_limit.per_timespan
parameter, for which the
default is every hour (1h
); the number of hash tree builds is
specified by anti_entropy.tree.build_limit.number
, for which the
default is 1.
Write Buffer Size
While you are free to choose the backend for data storage in Riak,
background AAE processes use LevelDB. You can adjust the size of the
write buffer used by LevelDB for hash tree generation using the
anti_entropy.write_buffer_size
parameter. The default is 4MB
.
Open Files and Concurrency Limits
The anti_entropy.concurrency_limit
parameter determines how many AAE
cross-node information exchanges or hash tree builds can happen
concurrently. The default is 2
.
The anti_entropy.max_open_files
parameter sets an open-files limit for
AAE-related background tasks, analogous to open files limit settings used in operating systems. The default is 20
.