Apache Cassandra relies on a number of techniques from Amazon’s Dynamo distributed storage key-value system. Each node in the Dynamo system has three main components:
Request coordination over a partitioned dataset
Ring membership and failure detection
A local persistence (storage) engine
Cassandra primarily draws from the first two clustering components, while using a storage engine based on a Log Structured Merge Tree (LSM). In particular, Cassandra relies on Dynamo style:
Dataset partitioning using consistent hashing
Multi-master replication using versioned data and tunable consistency
Distributed cluster membership and failure detection via a gossip protocol
Incremental scale-out on commodity hardware
Cassandra was designed this way to meet large-scale (PiB+) business-critical storage requirements. In particular, as applications demanded full global replication of petabyte scale datasets along with always available low-latency reads and writes, it became imperative to design a new kind of database model as the relational database systems of the time struggled to meet the new requirements of global scale applications.
Cassandra achieves horizontal scalability by partitioning all data stored in the system using a hash function. Each partition is replicated to multiple physical nodes, often across failure domains such as racks and even datacenters. As every replica can independently accept mutations to every key that it owns, every key must be versioned. Unlike in the original Dynamo paper where deterministic versions and vector clocks were used to reconcile concurrent updates to a key, Cassandra uses a simpler last write wins model where every mutation is timestamped (including deletes) and then the latest version of data is the "winning" value. Formally speaking, Cassandra uses a Last-Write-Wins Element-Set conflict-free replicated data type for each CQL row, or en.wikipedia.org/wiki/Conflict-free_replicated_data_type LWW-Element-Set_(Last-Write-Wins-Element-Set)[LWW-Element-Set CRDT], to resolve conflicting mutations on replica sets.
Cassandra partitions data over storage nodes using a special form of hashing called consistent hashing. In naive data hashing, you typically allocate keys to buckets by taking a hash of the key modulo the number of buckets. For example, if you want to distribute data to 100 nodes using naive hashing you might assign every node to a bucket between 0 and 100, hash the input key modulo 100, and store the data on the associated bucket. In this naive scheme, however, adding a single node might invalidate almost all of the mappings.
Cassandra instead maps every node to one or more tokens on a continuous hash ring, and defines ownership by hashing a key onto the ring and then "walking" the ring in one direction, similar to the Chord algorithm. The main difference of consistent hashing to naive data hashing is that when the number of nodes (buckets) to hash into changes, consistent hashing only has to move a small fraction of the keys.
For example, if we have an eight node cluster with evenly spaced tokens, and a replication factor (RF) of 3, then to find the owning nodes for a key we first hash that key to generate a token (which is just the hash of the key), and then we "walk" the ring in a clockwise fashion until we encounter three distinct nodes, at which point we have found all the replicas of that key. This example of an eight node cluster with gRF=3 can be visualized as follows:
You can see that in a Dynamo like system, ranges of keys, also known as token ranges, map to the same physical set of nodes. In this example, all keys that fall in the token range excluding token 1 and including token 2 (grange(t1, t2]) are stored on nodes 2, 3 and 4.
Simple single token consistent hashing works well if you have many
physical nodes to spread data over, but with evenly spaced tokens and a
small number of physical nodes, incremental scaling (adding just a few
nodes of capacity) is difficult because there are no token selections
for new nodes that can leave the ring balanced. Cassandra seeks to avoid
token imbalance because uneven token ranges lead to uneven request load.
For example, in the previous example there is no way to add a ninth
token without causing imbalance; instead we would have to insert
tokens in the midpoints of the existing ranges.
The Dynamo paper advocates for the use of "virtual nodes" to solve this imbalance problem. Virtual nodes solve the problem by assigning multiple tokens in the token ring to each physical node. By allowing a single physical node to take multiple positions in the ring, we can make small clusters look larger and therefore even with a single physical node addition we can make it look like we added many more nodes, effectively taking many smaller pieces of data from more ring neighbors when we add even a single node.
Cassandra introduces some nomenclature to handle these concepts:
Token: A single position on the dynamo style hash ring.
Endpoint: A single physical IP and port on the network.
Host ID: A unique identifier for a single "physical" node, usually present at one gEndpoint and containing one or more gTokens.
Virtual Node (or vnode): A gToken on the hash ring owned by the same physical node, one with the same gHost ID.
The mapping of Tokens to Endpoints gives rise to the Token Map where Cassandra keeps track of what ring positions map to which physical endpoints. For example, in the following figure we can represent an eight node cluster using only four physical nodes by assigning two tokens to every node:
Multiple tokens per physical node provide the following benefits:
When a new node is added it accepts approximately equal amounts of data from other nodes in the ring, resulting in equal distribution of data across the cluster.
When a node is decommissioned, it loses data roughly equally to other members of the ring, again keeping equal distribution of data across the cluster.
If a node becomes unavailable, query load (especially token aware query load), is evenly distributed across many other nodes.
Multiple tokens, however, can also have disadvantages:
Every token introduces up to
2 * (RF - 1)additional neighbors on the token ring, which means that there are more combinations of node failures where we lose availability for a portion of the token ring. The more tokens you have, the higher the probability of an outage.
Cluster-wide maintenance operations are often slowed. For example, as the number of tokens per node is increased, the number of discrete repair operations the cluster must do also increases.
Performance of operations that span token ranges could be affected.
Note that in Cassandra
2.x, the only token allocation algorithm
available was picking random tokens, which meant that to keep balance
the default number of tokens per node had to be quite high, at
This had the effect of coupling many physical endpoints together,
increasing the risk of unavailability. That is why in
3.x + the new
deterministic token allocator was added which intelligently picks tokens
such that the ring is optimally balanced while requiring a much lower
number of tokens per physical node.
Cassandra replicates every partition of data to many nodes across the
cluster to maintain high availability and durability. When a mutation
occurs, the coordinator hashes the partition key to determine the token
range the data belongs to and then replicates the mutation to the
replicas of that data according to the
All replication strategies have the notion of a replication factor
RF), which indicates to Cassandra how many copies of the partition
should exist. For example with a
RF=3 keyspace, the data will be
written to three distinct replicas. Replicas are always chosen such
that they are distinct physical nodes which is achieved by skipping
virtual nodes if needed. Replication strategies may also choose to skip
nodes present in the same failure domain such as racks or datacenters so
that Cassandra clusters can tolerate failures of whole racks and even
datacenters of nodes.
Cassandra supports pluggable replication strategies, which determine
which physical nodes act as replicas for a given token range. Every
keyspace of data has its own replication strategy. All production
deployments should use the
NetworkTopologyStrategy while the
SimpleStrategy replication strategy is useful only for testing
clusters where you do not yet know the datacenter layout of the cluster.
NetworkTopologyStrategy requires a specified replication factor
for each datacenter in the cluster. Even if your cluster only uses a
NetworkTopologyStrategy is recommended over
SimpleStrategy to make it easier to add new physical or virtual
datacenters to the cluster later, if required.
In addition to allowing the replication factor to be specified
individually by datacenter,
NetworkTopologyStrategy also attempts to
choose replicas within a datacenter from different racks as specified by
Snitch. If the number of racks is greater than or equal
to the replication factor for the datacenter, each replica is guaranteed
to be chosen from a different rack. Otherwise, each rack will hold at
least one replica, but some racks may hold more than one. Note that this
rack-aware behavior has some potentially
implications. For example, if there are not an even number of nodes in
each rack, the data load on the smallest rack may be much higher.
Similarly, if a single node is bootstrapped into a brand new rack, it
will be considered a replica for the entire ring. For this reason, many
operators choose to configure all nodes in a single availability zone or
similar failure domain as a single "rack".
SimpleStrategy allows a single integer
replication_factor to be
defined. This determines the number of nodes that should contain a copy
of each row. For example, if
replication_factor is 3, then three
different nodes should store a copy of each row.
SimpleStrategy treats all nodes identically, ignoring any configured
datacenters or racks. To determine the replicas for a token range,
Cassandra iterates through the tokens in the ring, starting with the
token range of interest. For each token, it checks whether the owning
node has been added to the set of replicas, and if it has not, it is
added to the set. This process continues until
distinct nodes have been added to the set of replicas.
Transient replication is an experimental feature in Cassandra 4.0 not present in the original Dynamo paper. This feature allows configuration of a subset of replicas to replicate only data that hasn’t been incrementally repaired. This configuration decouples data redundancy from availability. For instance, if you have a keyspace replicated at RF=3, and alter it to RF=5 with two transient replicas, you go from tolerating one failed replica to tolerating two, without corresponding increase in storage usage. Now, three nodes will replicate all the data for a given token range, and the other two will only replicate data that hasn’t been incrementally repaired.
To use transient replication, first enable the option in
cassandra.yaml. Once enabled, both
NetworkTopologyStrategy can be configured to transiently replicate
data. Configure it by specifying replication factor as
NetworkTopologyStrategy support configuring transient replication.
Transiently replicated keyspaces only support tables created with
read_repair set to
NONE; monotonic reads are not currently
supported. You also can’t use
LWT, logged batches, or counters in 4.0.
You will possibly never be able to use materialized views with
transiently replicated keyspaces and probably never be able to use
secondary indices with them.
Transient replication is an experimental feature that is not ready for production use. The expected audience is experienced users of Cassandra capable of fully validating a deployment of their particular application. That means being able check that operations like reads, writes, decommission, remove, rebuild, repair, and replace all work with your queries, data, configuration, operational practices, and availability requirements.
Anticipated additional features in
4.next are support for monotonic reads with
transient replication, as well as LWT, logged batches, and counters.
Cassandra uses mutation timestamp versioning to guarantee eventual consistency of data. Specifically all mutations that enter the system do so with a timestamp provided either from a client clock or, absent a client provided timestamp, from the coordinator node’s clock. Updates resolve according to the conflict resolution rule of last write wins. Cassandra’s correctness does depend on these clocks, so make sure a proper time synchronization process is running such as NTP.
Cassandra applies separate mutation timestamps to every column of every row within a CQL partition. Rows are guaranteed to be unique by primary key, and each column in a row resolve concurrent mutations according to last-write-wins conflict resolution. This means that updates to different primary keys within a partition can actually resolve without conflict! Furthermore the CQL collection types such as maps and sets use this same conflict free mechanism, meaning that concurrent updates to maps and sets are guaranteed to resolve as well.
As replicas in Cassandra can accept mutations independently, it is
possible for some replicas to have newer data than others. Cassandra has
many best-effort techniques to drive convergence of replicas including
Replica read repair <read-repair> in the read path and
Hinted handoff <hints> in the write path.
These techniques are only best-effort, however, and to guarantee
eventual consistency Cassandra implements
repair <repair> where replicas calculate hierarchical hash-trees over
their datasets called Merkle
trees that can then be compared across replicas to identify mismatched
data. Like the original Dynamo paper Cassandra supports full repairs
where replicas hash their entire dataset, create Merkle trees, send them
to each other and sync any ranges that don’t match.
Unlike the original Dynamo paper, Cassandra also implements sub-range repair and incremental repair. Sub-range repair allows Cassandra to increase the resolution of the hash trees (potentially down to the single partition level) by creating a larger number of trees that span only a portion of the data range. Incremental repair allows Cassandra to only repair the partitions that have changed since the last repair.
Cassandra supports a per-operation tradeoff between consistency and
availability through Consistency Levels. Cassandra’s consistency
levels are a version of Dynamo’s
R + W > N consistency mechanism where
operators could configure the number of nodes that must participate in
R) and writes (
W) to be larger than the replication factor
N). In Cassandra, you instead choose from a menu of common
consistency levels which allow the operator to pick
without knowing the replication factor. Generally writes will be visible
to subsequent reads when the read consistency level contains enough
nodes to guarantee a quorum intersection with the write consistency
The following consistency levels are available:
Only a single replica must respond.
Two replicas must respond.
Three replicas must respond.
A majority (n/2 + 1) of the replicas must respond.
All of the replicas must respond.
A majority of the replicas in the local datacenter (whichever datacenter the coordinator is in) must respond.
A majority of the replicas in each datacenter must respond.
Only a single replica must respond. In a multi-datacenter cluster, this also gaurantees that read requests are not sent to replicas in a remote datacenter.
A single replica may respond, or the coordinator may store a hint. If a hint is stored, the coordinator will later attempt to replay the hint and deliver the mutation to the replicas. This consistency level is only accepted for write operations.
Write operations are always sent to all replicas, regardless of consistency level. The consistency level simply controls how many responses the coordinator waits for before responding to the client.
For read operations, the coordinator generally only issues read commands to enough replicas to satisfy the consistency level. The one exception to this is when speculative retry may issue a redundant read request to an extra replica if the original replicas have not responded within a specified time window.
It is common to pick read and write consistency levels such that the
replica sets overlap, resulting in all acknowledged writes being visible
to subsequent reads. This is typically expressed in the same terms
Dynamo does, in that
W + R > RF, where
W is the write consistency
R is the read consistency level, and
RF is the replication
factor. For example, if
RF = 3, a
QUORUM request will require
responses from at least
2/3 replicas. If
QUORUM is used for both
writes and reads, at least one of the replicas is guaranteed to
participate in both the write and the read request, which in turn
guarantees that the quorums will overlap and the write will be visible
to the read.
In a multi-datacenter environment,
LOCAL_QUORUM can be used to provide
a weaker but still useful guarantee: reads are guaranteed to see the
latest write from within the same datacenter. This is often sufficient
as clients homed to a single datacenter will read their own writes.
If this type of strong consistency isn’t required, lower consistency
ONE may be used to improve throughput,
latency, and availability. With replication spanning multiple
LOCAL_ONE is typically less available than
ONE but is
faster as a rule. Indeed
ONE will succeed if a single replica is
available in any datacenter.
The replication protocols and dataset partitioning rely on knowing which nodes are alive and dead in the cluster so that write and read operations can be optimally routed. In Cassandra liveness information is shared in a distributed fashion through a failure detection mechanism based on a gossip protocol.
Gossip is how Cassandra propagates basic cluster bootstrapping
information such as endpoint membership and internode network protocol
versions. In Cassandra’s gossip system, nodes exchange state information
not only about themselves but also about other nodes they know about.
This information is versioned with a vector clock of
(generation, version) tuples, where the generation is a monotonic
timestamp and version is a logical clock the increments roughly every
second. These logical clocks allow Cassandra gossip to ignore old
versions of cluster state just by inspecting the logical clocks
presented with gossip messages.
Every node in the Cassandra cluster runs the gossip task independently and periodically. Every second, every node in the cluster:
Updates the local node’s heartbeat state (the version) and constructs the node’s local view of the cluster gossip endpoint state.
Picks a random other node in the cluster to exchange gossip endpoint state with.
Probabilistically attempts to gossip with any unreachable nodes (if one exists)
Gossips with a seed node if that didn’t happen in step 2.
When an operator first bootstraps a Cassandra cluster they designate certain nodes as seed nodes. Any node can be a seed node and the only difference between seed and non-seed nodes is seed nodes are allowed to bootstrap into the ring without seeing any other seed nodes. Furthermore, once a cluster is bootstrapped, seed nodes become hotspots for gossip due to step 4 above.
As non-seed nodes must be able to contact at least one seed node in order to bootstrap into the cluster, it is common to include multiple seed nodes, often one for each rack or datacenter. Seed nodes are often chosen using existing off-the-shelf service discovery mechanisms.
Nodes do not have to agree on the seed nodes, and indeed once a cluster is bootstrapped, newly launched nodes can be configured to use any existing nodes as seeds. The only advantage to picking the same nodes as seeds is it increases their usefullness as gossip hotspots.
Currently, gossip also propagates token metadata and schema version information. This information forms the control plane for scheduling data movements and schema pulls. For example, if a node sees a mismatch in schema version in gossip state, it will schedule a schema sync task with the other nodes. As token information propagates via gossip it is also the control plane for teaching nodes which endpoints own what data.
Gossip forms the basis of ring membership, but the failure detector
ultimately makes decisions about if nodes are
DOWN. Every node
in Cassandra runs a variant of the
Accrual Failure Detector, in which every node is constantly making an
independent decision of if their peer nodes are available or not. This
decision is primarily based on received heartbeat state. For example, if
a node does not see an increasing heartbeat from a node for a certain
amount of time, the failure detector "convicts" that node, at which
point Cassandra will stop routing reads to it (writes will typically be
written to hints). If/when the node starts heartbeating again, Cassandra
will try to reach out and connect, and if it can open communication
channels it will mark that node as available.
Cassandra will never remove a node from gossip state without
explicit instruction from an operator via a decommission operation or a
new node bootstrapping with a
replace_address_first_boot option. This
choice is intentional to allow Cassandra nodes to temporarily fail
without causing data to needlessly re-balance. This also helps to
prevent simultaneous range movements, where multiple replicas of a token
range are moving at the same time, which can violate monotonic
consistency and can even cause data loss.
Cassandra scales-out to meet the requirements of growth in data size and request rates. Scaling-out means adding additional nodes to the ring, and every additional node brings linear improvements in compute and storage. In contrast, scaling-up implies adding more capacity to the existing database nodes. Cassandra is also capable of scale-up, and in certain environments it may be preferable depending on the deployment. Cassandra gives operators the flexibility to chose either scale-out or scale-up.
One key aspect of Dynamo that Cassandra follows is to attempt to run on commodity hardware, and many engineering choices are made under this assumption. For example, Cassandra assumes nodes can fail at any time, auto-tunes to make the best use of CPU and memory resources available and makes heavy use of advanced compression and caching techniques to get the most storage out of limited memory and storage capabilities.
Cassandra, like Dynamo, chooses not to provide cross-partition transactions that are common in SQL Relational Database Management Systems (RDBMS). This both gives the programmer a simpler read and write API, and allows Cassandra to more easily scale horizontally since multi-partition transactions spanning multiple nodes are notoriously difficult to implement and typically very latent.
Instead, Cassanda chooses to offer fast, consistent, latency at any scale for single partition operations, allowing retrieval of entire partitions or only subsets of partitions based on primary key filters. Furthermore, Cassandra does support single partition compare and swap functionality via the lightweight transaction CQL API.
Cassandra, in a slight departure from Dynamo, chooses a storage interface that is more sophisticated then "simple key value" stores but significantly less complex than SQL relational data models. Cassandra presents a wide-column store interface, where partitions of data contain multiple rows, each of which contains a flexible set of individually typed columns. Every row is uniquely identified by the partition key and one or more clustering keys, and every row can have as many columns as needed.
This allows users to flexibly add new columns to existing datasets as new requirements surface. Schema changes involve only metadata changes and run fully concurrently with live workloads. Therefore, users can safely add columns to existing Cassandra databases while remaining confident that query performance will not degrade.