In this post, I discuss DeCandia et al’s Dynamo paper, and Amazon’s DynamoDB service based on the paper.


DeCandia et al’s Dyanamo is a distributed key-value store remarkable for it’s entirely decentralized architecture, SLAs that focus on 99.9th percentile latency, emphasis on never losing writes, and the notorious sloppy quorums. Supporting decentralized architecture requried several innovations, such as anti-entropy protocols like hinted handoff and read repair. Dynamo was originally built as an infrastructure rather than a service. To quote from the paper: “Each service that uses Dynamo runs its own Dyanamo instances”, “[Dyamo] is built for a trusted environment”.

Dynamo is a simple key-value store with flat keyspace. The keyspace is assumed to be quite large, hence partitioned across multiple nodes. The partitioning is done by a consistent hashing algorithm that envisions the keyspace as a ring, and makes a node responsible for an arc (usually an adjacent one) on the ring. Hence, each instance is called a Dynamo ring. For durability and high availability, each key is replicated across multiple nodes (called it’s replication factor, which is usually set to 3), often spanning multiple data centers. Thus, a ring spans multiple geographic locations. Each node in the ring maintains a ring membership information, capturing the current node’s view of the ring. This information is regularly updated via gossip. Any node in the ring accepts reads and writes for any key in the keyspace. If the node is not one of the replicas of the key, it forwards the request to a replica. For this purpose, each node maintains a preference list of nodes for each partition of the keyspace, which is consulted to decide which node should serve a request. Even when all nodes have a consistent view of ring membership, preference lists maintained by different nodes can still be different. For instance, if nodes A and B are both replicas for key k, then preference list for k in A has A at the top, whereas in B’s list has B at the top. In general, preference list for a partition at a node is ordered so as to minimize the latency to serve requests on key k starting from that node.

Like all distributed databases, the durability of each read (R) and write (W) is configurable. Under normal circumstances, if R+W N, where N is the replication factor, we get a system with quorum consistency that, for example, supports Read-My-Writes (RMW) guarantee. However, Dynamo’s quorums are not traditional (Lamport) quorums. In the event of a network partition, even when none of the replicas for a key are reachable, Dynamo still accepts writes for the key, allowing the reachable nodes to act as makeshift replicas. While this behavior in itself is not suprising, considering that data stores are often designed to be available even if one node is reachable, Dynamo allows the reachable nodes to organize themselves into a ring, and form quorums on each side of the network partition! Such quorums are called sloppy quorums. Thus even if writes and reads from a session are successfully executed with quorum consistency, we still may not get RMW.

Network partitions or otherwise, concurrent updates to a key are possible. Dynamo uses vector clocks to identify multiple versions of an object corresponding to a key. If vector clocks of two versions are totally ordered, then conflict resolution is trivial. If they are not, then Dynamo keeps both the versions and lets the application handle conflicts. As I shall demonstrate later, keeping multiple versions is particularly important in case of Dynamo, otherwise it may lose the entire set of writes submitted on one side of a network partition after conflict resolution.


Present day DynamoDB is a multi-tenant hosted service offered by Amazon. The data model is more-or-less flat key-value, with some new additions (souce: core components of DynamoDB):

  • While the unique primary key of a data item also used to be its partition key, the primary key can now be defined as a combination of partition key (also called the hash key) and a sort key (also called the range key). Their combination needs to be unique for a data item.
  • The value needs to be a JSON with any number of attributes. While the primary key schema of each data item is fixed in a table, the schema of values is not. However, DynamoDB takes cognizance of attributes when a JSON is being stored, allowing secondary indexes to be created, and the attributes to be queried.

Some more relevant points from its documentation:

  • Amazon DynamoDB stores three geographically distributed replicas of each table to enable high availability and data durability.
  • Consistency across all copies of data is usually reached within a second.
  • A strongly consistent read returns a result that reflects all writes that received a successful response prior to the read.

It is not clear how strongly consistent read is implemented in DynamoDB. If it has to return the value of previous write regardless of the write consistency, then its consistency level has to be ALL. Alternatively, if it only returns values of previous strong writes, then both read and write can be QUORUM (strict quorum; sloppy won’t do). UPDATE: A stackoverflow user suggests that (a). DynamoDB only has only strict quorums (no sloppy quorums and hinted handoffs), (b). All writes in DynamoDB are written to a quorum of replicas, (c). By default, reads are served by a single replica, and (d). strong reads are served by a quorum of replicas. This makes sense.

Apart from regular writes, DynamoDB supports atomic in-place updates of attributes of a data item. For example, we can update, user.address, and user.telephone for a given in a single update api call to DynamoDB (one round-trip). The update can also be conditional, in which case it is applied if and only if the current value of the data item meets certain conditions. Conditional update is presumably implemented via paxos, giving it CAS semantics. To help construct in-place conditional update operations, DynamoDB defines a fairly expressive language for conditional expressions and update expressions (A primer on reading and writing items using expressions is here). The documentation says that conditional update is idempotent, because CAS is idempotent, but DynamoDB’s conditional update is more general than CAS. In general, conditional update is idempotent only if the update negates the condition.

Through conditional updates, DynamoDB already offers serializable transactions on (multiple attributes of) a single data item. But, this is only the beginning! As it turns out, DynamoDB also implements full-fledged multi-object transactions with variable isolation levels! (more here). DynamoDB currently defines three different isolation levels, without making any reference to the ANSI SQL standard. As described by the documentation:

  • Fully isolatated reads are performed through obtaining locks during a transaction, just like writes.
  • Committed reads are provide a consistency guarantee similar to eventually consistent reads, and are performed by reading the old copy of the item if a lock is detected.
  • Uncommitted reads (also known as dirty reads) are the cheapest, but are the most dangerous, since they may return data that will later be rolled back.

Roughly, they correspond to ANSI SQL Serializable, Read Committed, and Read Uncommitted isolation levels, respectively. Note that, unlike relational databases, where isolation level is set per-transaction, DynamoDB allows isolation level to be set per-read in a transaction. This is why there is no isolation level corresponding to Repeatable Read. Nonetheless, more analysis is needed to determine the exact guarantees offered by each of these isolation levels.

So, to summarize, DynamoDB offers quorum writes and weak reads by default. Application can request strong reads to get RMW, but they are twice as expensive. An update operation is a quorum write that lets (multiple attributes of) a data item to be upated atomically. It consumes one write capacity unit. So does a conditional update, that goes beyond CAS semantics to enable serializable transactions on a single data item. However, conditional updates may often fail, and retries consume more write units, making it expensive. Multi-item transactions with variable isolation levels are possible, and writes from transactions are very expensive. As per the documentation, a write from a transaction consumes roughtly 7N+4 write capacity units, where N is the size of the transaction. The cost model for each isolation level is not known, but fully isolated transactions are most expensive because it comes “at the cost of turning reads into relatively expensive writes.”

Sample Applications

  • This blog describes a simple game, where two players advance their positions via conditional updates.
  • Product catalogue case study is described here.
  • Tic-Tac-Toe game developed via conditional updates and multi-key transactions is described in this blog. The example is also discussed in this AWS re:Invent 2013 video. The same video also describes Dropcam’s experience using DynamoDB. The
  • Session logouts due to the lack of RMW is described here.
  • Manu transaction examples here.