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<article class="doc">
<h1 class="page">Distributed James Server &mdash; Consistency Model</h1>
<div id="preamble">
<div class="sectionbody">
<div class="paragraph">
<p>This page presents the consistency model used by the Distributed Server and
points to the tools built around it.</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_data_replication"><a class="anchor" href="#_data_replication"></a>Data Replication</h2>
<div class="sectionbody">
<div class="paragraph">
<p>The Distributed Server relies on different storage technologies, all having their own
consistency models.</p>
</div>
<div class="paragraph">
<p>These data stores replicate data in order to enforce some level of availability. We call
this process replication. By consistency, we mean the ability for all replica to hold the
same data. By availability, we mean the ability for a replica to answer a request.</p>
</div>
<div class="paragraph">
<p>In distributed systems, <a href="https://en.wikipedia.org/wiki/CAP_theorem">according to the CAP theorem</a>,
as we will necessarily encounter network partitions, then trade-offs need to be made between
consistency and availability.</p>
</div>
<div class="paragraph">
<p>This section details this trade-off for data stores used by the Distributed Server.</p>
</div>
<div class="sect2">
<h3 id="_cassandra_consistency_model"><a class="anchor" href="#_cassandra_consistency_model"></a>Cassandra consistency model</h3>
<div class="paragraph">
<p><a href="https://cassandra.apache.org/">Cassandra</a> is an
<a href="https://en.wikipedia.org/wiki/Eventual_consistency">eventually consistent</a> data store.
This means that replica can hold diverging data, but are guaranteed to converge over time.</p>
</div>
<div class="paragraph">
<p>Several mechanisms are built in Cassandra to enforce this convergence, and need to be
leveraged by <strong>Distributed Server Administrator</strong>. Namely
<a href="https://docs.datastax.com/en/dse/5.1/dse-admin/datastax_enterprise/tools/nodetool/toolsRepair.html">nodetool repair</a>,
<a href="https://cassandra.apache.org/doc/latest/operating/hints.html">Hinted hand-off</a> and
<a href="https://cassandra.apache.org/doc/latest/operating/read_repair.html">Read repair</a>.</p>
</div>
<div class="paragraph">
<p>The Distributed Server tries to mitigate inconsistencies by relying on
<a href="https://docs.datastax.com/en/archived/cassandra/3.0/cassandra/dml/dmlConfigConsistency.html">QUORUM</a> read and write levels.
This means that a majority of replica are needed for read and write operations to be performed.</p>
</div>
<div class="paragraph">
<p>Critical business operations, like UID allocation, rely on strong consistency mechanisms brought by
<a href="https://www.datastax.com/blog/2013/07/lightweight-transactions-cassandra-20">lightweight transaction</a>.</p>
</div>
<div class="sect3">
<h4 id="_about_multi_data_center_setups"><a class="anchor" href="#_about_multi_data_center_setups"></a>About multi data-center setups</h4>
<div class="paragraph">
<p>As strong consistency is required for some operation, especially regarding IMAP monotic UID and MODSEQ generation,
and as lightweight transactions are slow across data centers, running James with a
<a href="https://docs.datastax.com/en/ddac/doc/datastax_enterprise/production/DDACmultiDCperWorkloadType.html">multi data-center</a>
Cassandra setup is discouraged.</p>
</div>
<div class="paragraph">
<p>However, <a href="../configure/cassandra.html" class="xref page">this page</a> enables setting alternative read level,
which could be acceptable regarding limited requirements. <code>LOCAL_QUORUM</code> coupled with <code>LOCAL_SERIAL</code>
is likely the only scalable setup. Some options were added to turn off SERIAL consistency usage for message
and mailbox management. However, the use of Lightweight Transaction cannot be disabled for UIDs and ModSeqs.</p>
</div>
<div class="paragraph">
<p>Running the Distributed Server IMAP server in a multi datacenter setup will likely result either in data loss,
or very slow operations - as we rely on monotic UID generation, without strong consistency, UIDs could be allocated
several times.</p>
</div>
<div class="paragraph">
<p>We did wire a multi-DC friendly distributed, POP3 only server that leverages acceptable performance while staying
consistent. This is achieved by having a reduced feature set - supporting only the POP3 server and using messageIds as
identifiers (generated without synchronisation using TimeUUIDs). You can find this application
<a href="https://github.com/apache/james-project/tree/master/server/apps/distributed-pop3-app">on GitHub</a>. In the future,
JMAP support could be added, but requires followup developments as some components critically depends on UIDs
(for instance the search).</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_opensearch_consistency_model"><a class="anchor" href="#_opensearch_consistency_model"></a>OpenSearch consistency model</h3>
<div class="paragraph">
<p>OpenSearch relies on <a href="https://www.elastic.co/blog/a-new-era-for-cluster-coordination-in-elasticsearch">strong consistency</a>
with home grown algorithm.</p>
</div>
<div class="paragraph">
<p>The 6.x release line, that the distributed server is using is known to be slow to recover from failures.</p>
</div>
<div class="paragraph">
<p>Be aware that data is asynchronously indexed in OpenSearch, changes will be eventually visible.</p>
</div>
</div>
<div class="sect2">
<h3 id="_rabbitmq_consistency_model"><a class="anchor" href="#_rabbitmq_consistency_model"></a>RabbitMQ consistency model</h3>
<div class="paragraph">
<p>The Distributed Server relies out of the box on a single RabbitMQ server, thus consistency concerns
are not (yet) applicable. Availability concerns are applicable.</p>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_denormalization"><a class="anchor" href="#_denormalization"></a>Denormalization</h2>
<div class="sectionbody">
<div class="paragraph">
<p>In Cassandra, data needs to be structured to match the read patterns. To support several conflicting
read patterns, the data needs to be duplicated into different structures. This process is called
denormalization.</p>
</div>
<div class="paragraph">
<p>While data can be consistent at the table level, some inconsistencies can sneak in at the applicative
level across denormalization tables.</p>
</div>
<div class="paragraph">
<p>We write to a "table of truth" first, then duplicate the data to denormalization tables.</p>
</div>
<div class="paragraph">
<p>The Distributed server offers several mechanisms to mitigate these inconsistencies:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Writes to denormalization tables are retried.</p>
</li>
<li>
<p>Some <a href="../operate/guide.html#_solving_cassandra_inconsistencies" class="xref page">SolveInconsistencies tasks</a> are exposed and are able to heal a given denormalization table.
They reset the "deduplication tables" content to the "table of truth" content.</p>
</li>
<li>
<p><a href="https://github.com/apache/james-project/blob/master/src/adr/0042-applicative-read-repairs.md">Read repairs</a>,
when implemented for a given denormalization, enables auto-healing. When an inconsistency is detected, They reset the
"deduplication tables" entry to the "table of truth" entry.</p>
</li>
</ul>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_consistency_across_data_stores"><a class="anchor" href="#_consistency_across_data_stores"></a>Consistency across data stores</h2>
<div class="sectionbody">
<div class="paragraph">
<p>The Distributed Server leverages several data stores:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Cassandra is used for metadata storage</p>
</li>
<li>
<p>OpenSearch for search</p>
</li>
<li>
<p>Object Storage for large object storage</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>Thus the Distributed Server also offers mechanisms to enforce consistency across data stores.</p>
</div>
<div class="sect2">
<h3 id="_write_path_organisation"><a class="anchor" href="#_write_path_organisation"></a>Write path organisation</h3>
<div class="paragraph">
<p>The primary data stores are composed of Cassandra for metadata and Object storage for binary data.</p>
</div>
<div class="paragraph">
<p>To ensure the data referenced in Cassandra is pointing to a valid object in the object store, we write
the object store payload first, then write the corresponding metadata in Cassandra.</p>
</div>
<div class="paragraph">
<p>Such a procedure avoids metadata pointing to unexisting blobs, however might lead to some unreferenced
blobs.</p>
</div>
</div>
<div class="sect2">
<h3 id="_cassandra_opensearch"><a class="anchor" href="#_cassandra_opensearch"></a>Cassandra &lt;&#8658; OpenSearch</h3>
<div class="paragraph">
<p>After being written to the primary stores (namely Cassandra &amp; Object Storage), email content is
asynchronously indexed into OpenSearch.</p>
</div>
<div class="paragraph">
<p>This process, called the EventBus, which retries temporary errors, and stores transient errors for
later admin-triggered retries is described further <a href="../operate/guide.html#_mailbox_event_bus" class="xref page">here</a>.
His role is to spread load and limit inconsistencies.</p>
</div>
<div class="paragraph">
<p>Furthermore, some <a href="../operate/guide.html#_usual_troubleshooting_procedures" class="xref page">re-indexing tasks</a>
enables to re-synchronise OpenSearch content with the primary data stores</p>
</div>
</div>
</div>
</div>
</article>
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