Scalability is one of the main drivers of the Nosql movement. As such,it encompasses distributed system coordination,failover,resource management and many other capabilities. It sounds like a big umbrella,and it is. Although it can hardly be said that Nosql movement brought fundamentally new techniques into distributed data processing,it triggered an avalanche of practical studies and real-life trials of different combinations of protocols and algorithms. These developments gradually highlight a system of relevant database building blocks with proven practical efficiency. In this article I’m trying to provide more or less systematic description of techniques related to distributed operations in Nosql databases.

In the rest of this article we study a number of distributed activities like replication of failure detection that could happen in a database. These activities,highlighted in bold below,are grouped into three major sections:

• Data Consistency. Historically,Nosql paid a lot of attention to tradeoffs between consistency,fault-tolerance and performance to serve geographically distributed systems,low-latency or highly available applications. Fundamentally,these tradeoffs spin around data consistency,so this section is devoteddata replicationanddata repair.
• Data Placement. A database should accommodate itself to different data distributions,cluster topologies and hardware configurations. In this section we discuss how todistribute or rebalance datain such a way that failures are handled rapidly,persistence guarantees are maintained,queries are efficient,and system resource like RAM or disk space are used evenly throughout the cluster.
• System Coordination. Coordination techniques likeleader electionare used in many databases to implements fault-tolerance and strong data consistency. However,even decentralized databases typically track their global state,detect failures and topology changes. This section describes several important techniques that are used to keep the system in a coherent state.

Data Consistency

It is well known and fairly obvIoUs that in geographically distributed systems or other environments with probable network partitions or delays it is not generally possible to maintain high availability without sacrificing consistency because isolated parts of the database have to operate independently in case of network partition. This fact is often referred to as the CAP theorem. However,consistency is a very expensive thing in distributed systems,so it can be traded not only to availability. It is often involved into multiple tradeoffs. To study these tradeoffs,we first note that consistency issues in distributed systems are induced by the replication and the spatial separation of coupled data,so we have to start with goals and desired properties of the replication:

• Availability. Isolated parts of the database can serve read/write requests in case of network partition.
• Read/Write latency. Read/Write requests are processes with a minimal latency.
• Read/Write scalability. Read/Write load can be balanced across multiple nodes.
• Fault-tolerance. Ability to serve read/write requests does not depend on availability of any particular node.
• Data persistence. Node failures within certain limits do not cause data loss.
• Consistency. Consistency is a much more complicated property than the prevIoUs ones,so we have to discuss different options in detail. It beyond this article to go deeply into theoretical consistency and concurrency models,so we use a very lean framework of simple properties.
  • Read-Write consistency. From the read-write perspective,the basic goal of a database is to minimize a replica convergence time (how long does it take to propagate an update to all replicas) and guarantee eventual consistency. Besides these weak guarantees,one can be interested in stronger consistency properties:
    • Read-after-write consistency. The effect of a write operation on data item X,will always be seen by a successive read operation on X.
    • Read-after-read consistency. If some client reads the value of a data item X,any successive read operation on X will always return that same or a more recent value.
  • Write-Write consistency. Write-write conflicts appear in case of database partition,so a database should either handle these conflicts somehow or guarantee that concurrent writes will not be processed by different partitions. From this perspective,a database can offer different consistency models:
    • Atomic Writes. If a database provides an API where a write request can only be an independent atomic assignment of a value,one possible way to avoid write-write conflicts is to pick the “most recent” version of each entity. This guarantees that all nodes will end up with the same version of data irrespectively to the order of updates which can be affected by network failures and delays. Data version can be specified by a timestamps or application-specific metric. This approach is used for example in Cassandra.
    • Atomic Read-modify-write. Applications often do a read-modify-write sequence instead of independent atomic writes. If two clients read the same version of data,modify it and write back concurrently,the latest update will silently override the first one in the atomic writes model. This behavior can be semantically inappropriate (for example,if both clients add a value to a list). A database can offer at least two solutions:
      • Conflict prevention. Read-modify-write can be thought as a particular case of transaction,so distributed locking or consensus protocols like PAXOS [20,21] are both a solution. This is a generic technique that can support both atomic read-modify-write semantics and arbitrary isolated transactions. An alternative approach is to prevent distributed concurrent writes entirely and route all writes of a particular data item to a single node (global master or shard master). To prevent conflicts,a database must sacrifice availability in case of network partitioning and stop all but one partition. This approach is used in many systems with strong consistency guarantees (e.g. most RDBMSs,HBase,MongoDB).
      • Conflict detection. A database track concurrent conflicting updates and either rollback one of the conflicting updates or preserve both versions for resolving on the client side. Concurrent updates are typically tracked by using vector clocks [19] (which can be though as a generalization of the optimistic locking) or by preserving an entire version history. This approach is used in systems like Riak,Voldemort,CouchDB.

Now let’s take a closer look at commonly used replication techniques and classify them in accordance with the described properties. The first figure below depicts logical relationships between different techniques and their coordinates in the system of the consistency-scalability-availability-latency tradeoffs. The second figure illustrates each technique in detail.

Let’s go through all these techniques moving from weak to strong consistency guarantees:

• (A,Anti-Entropy) Weakest consistency guarantees are provided by the following strategy. Writer updates any arbitrary selected replica. Reader reads any replica and sees the old data until a new version is not propagated via background anti-entropy protocol (more on anti-entropy protocols in the next section). The main properties of this approach are:
  • High propagation latency makes it quite impractical for data synchronization,so it is typically used only as an auxiliary background process that detects and repairs unplanned inconsistencies. However,databases like Cassandra use anti-entropy as a primary way to propagate information about database topology and other Metadata.
  • Consistency guarantees are poor: write-write conflicts and read-write discrepancies are very probable even in absence of failures.
  • Superior availability and robustness against network partitions. This schema provides good performance because individual updates are replaced by asynchronous batch processing.
  • Persistence guarantees are weak because new data are initially stored on a single replica.
• (B) An obvIoUs improvement of the prevIoUs schema is to send an update to all (available) replicas asynchronously as soon as the update request hits any replica. It can be considered as a kind of targeted anti-entropy.
  • In comparison with pure anti-entropy,this greatly improves consistency with a relatively small performance penalty. However,formal consistency and persistence guarantees remain the same.
  • If some replica is temporary unavailable due to network failures or node failure/replacement,updates should be eventually delivered to it by the anti-entropy process.
• (C) In the prevIoUs schema,failures can be handled better using the hinted handoff technique [8]. Updates that are intended for unavailable nodes are recorded on the coordinator or any other node with a hint that they should be delivered to a certain node as soon as it will become available. This improves persistence guarantees and replica convergence time.
• (D,Read One Write One) Since the carrier of hinted handoffs can fail before deferred updates were propagated,it makes sense to enforce consistency by so-called read repairs. Each read (or randomly selected reads) triggers an asynchronous process that requests a digest (a kind of signature/hash) of the requested data from all replicas and reconciles inconsistencies if detected. We use term ReadOne-WriteOne for combination of techniques A,B,C and D – they all do not provide strict consistency guarantees,but are efficient enough to be used in practice as an self-contained approach.
• (E,Read Quorum Write Quorum) The strategies above are heuristic enhancements that decrease replicas convergence time. To provide guarantees beyond eventual consistency,one has to sacrifice availability and guarantee an overlap between read and write sets. A common generalization is to write synchronously W replicas instead of one and touch R replicas during reading.
  • First,this allows one to manage persistence guarantees setting W>1.
  • Second,this improves consistency for R+W>N because synchronously written set will overlap with the set that is contacted during reading (in the figure above W=2,R=3,N=4),so reader will touch at least one fresh replica and select it as a result. This guarantees consistency if read and write requests are issued sequentially (e.g. by one client,read-your-writes consistency),but do not guarantee global read-after-read consistency. Consider an example in the figure below to see why reads can be inconsistent. In this example R=2,W=2,N=3. However,writing of two replicas is not transactional,so clients can fetch both old and new values until writing is not completed:

• Different values of R and W allows to trade write latency and persistence to read latency and vice versa.
• Concurrent writers can write to disjoint quorums if W<=N/2. Setting W>N/2 guarantees immediate conflict detection in Atomic Read-modify-write with rollbacks model.
• Strictly speaking,this schema is not tolerant to network partitions,although it tolerates failures of separate nodes. In practice,heuristics like sloppy quorum [8] can be used to sacrifice consistency provided by a standard quorum schema in favor of availability in certain scenarios.
• (F,Read All Write Quorum) The problem with read-after-read consistency can be alleviated by contacting all replicas during reading (reader can fetch data or check digests). This ensures that a new version of data becomes visible to the readers as soon as it appears on at least one node. Network partitions of course can lead to violation of this guarantee.
• (G,Master-Slave) The techniques above are often used to provide either Atomic Writes or Read-modify-write with Conflict Detection consistency levels. To achieve a Conflict Prevention level,one has to use a kind of centralization or locking. A simplest strategy is to use master-slave asynchronous replication. All writes for a particular data item are routed to a central node that executes write operations sequentially. This makes master a bottleneck,so it becomes crucial to partition data into independent shards to be scalable.
• (H,Transactional Read Quorum Write Quorum and Read One Write All) Quorum approach can also be reinforced by transactional techniques to prevent write-write conflicts. A well-known approach is to use two-phase commit protocol. However,two-phase commit is not perfectly reliable because coordinator failures can cause resource blocking. PAXOS commit protocol [20,21] is a more reliable alterative,but with a price or performance penalty. A small step forward and we end up with the Read One Write All approach where writes update all replicas in a transactional fashion. This approach provides strong fault-tolerant consistency but with a price of performance and availability.

It is worth noting that the analysis above highlights a number of tradeoffs:

• Consistency-availability tradeoff. This strict tradeoff is formalized by the CAP theorem. In case of network partition,a database should either stop all partitions except one or accept the possibility of data conflicts.
• Consistency-scalability tradeoff. One can see that even read-write consistency guarantees impose serIoUs limitations on a replica set scalability,and write-write conflicts can be handled in a relatively scalable fashion only in the Atomic Writes model. The Atomic Read-modify-write model introduces short casual dependencies between data and this immediately requires global locking to prevent conflicts. This shows thateven a slight spatial or casual dependency between data entries or operations could kill scalability,so separation of data into independent shards andcareful data modelingis extremely important for scalability.
• Consistency-latency tradeoff. As it was shown above,there exists a tendency to Read-All and Write-All techniques when strong consistency or persistence guarantees are provides by a database. These guarantees are clearly in inverse proportion to requests latency. Quorum techniques are a middle ground.
• Failover-consistency/scalability/latency tradeoff. It is interesting that contention between failover and consistency/scalability/latency is not really severe. Failures of up to N/2 nodes can often be tolerated with reasonable performance/consistency penalty. However,this tradeoff is visible,for example,in the difference between 2-phase commit and PAXOS protocols. Another example of this tradeoff is ability to lift certain consistency guarantees like read-your-writes using sticky sessions which complicate failover [22].

Anti-Entropy Protocols,Gossips

Let us start our study with the following problem statement:

There is a set of nodes and each data item is replicated to a subset of nodes. Each node serves update requests even if there is no network connection to other nodes. Each node periodically synchronizes its state with other nodes is such a way that if no updates take place for a long time,all replicas will gradually become consistent. How this synchronization should be organized – when synchronization is triggered,how a peer to synchronize with is chosen,what is the data exchange protocol? Let us assume that two nodes can always merge their versions of data selecting a newest version or preserving both versions for further application-side resolution.

This problem appears both in data consistency maintenance and in synchronization of a cluster state (propagation of the cluster membership information and so on). Although the problem above can be solved by means of a global coordinator that monitors a database and builds a global synchronization plan or schedule,decentralized databases take advantage of more fault-tolerant approach. The main idea is to use well-studied epidemic protocols [7] that are relatively simple,provide a pretty good convergence time,and can tolerate almost any failures or network partitions. Although there are different classes of epidemic algorithms,we focus on anti-entropy protocols because of their intensive usage in Nosql databases.

Anti-entropy protocols assume that synchronization is performed by a fixed schedule – every node regularly chooses another node at random or by some rule and exchanges database contents,resolving differences. There are three flavors of anti-entropy protocols: push,pull,and push-pull. The idea of the push protocol is to simply select a random peer and push a current state of data to it. In practice,it is quite silly to push the entire database,so nodes typically work in accordance with the protocol which is depicted in the figure below.

Node A which is initiator of synchronization prepares a digest (a set of checksums) which is a fingerprint of its data. Node B receives this digest,determines the difference between the digest and its local data and sends a digest of the difference back to A. Finally,A sends an update to B and B updates itself. Pull and push-pull protocols work similarly,as it shown in the figure above.

Anti-entropy protocols provide reasonable good convergence time and scalability. The following figure shows simulation results for propagation of an update in the cluster of 100 nodes. On each iteration,each node contacts one randomly selected peer.

One can see that the pull style provides better convergence than the push,and this can be proven theoretically [7]. Also,push has a problem with a “convergence tail” when a small percent of nodes remains unaffected during many iterations,although almost all nodes are already touched. The Push-Pull approach greatly improves efficiency in comparison with the original push or pulls techniques,so it is typically used in practice. Anti-entropy is scalable because the average conversion time grows as a logarithmic function of the cluster size.

class Counter {
   int[] plus
   int[] minus
   int NODE_ID

   increment() {
      plus[NODE_ID]++
   }

   decrement() {
      minus[NODE_ID]++
   }

   get() {
      return sum(plus) – sum(minus)
   }

   merge(Counter other) {
      for i in 1..MAX_ID {
         plus[i] = max(plus[i],other.plus[i])
         minus[i] = max(minus[i],other.minus[i])
      }
   }
}

If one does not have a database that supports data rebalancing internally,he probably will deploy several instances of the database to each node as it is shown in the section (B) of the figure above. This allows one to perform a manual cluster expansion by turning a separate instance off,copying it to a new node,and turning it on,as it is shown in the section (C). Although an automatic database is able to track each record separately,many systems including MongoDB,Oracle Coherence,and upcoming Redis Cluster use the described technique internally,i.e. group records into shards which are minimal units of migration for sake of efficiency. It is quite obvious that a number of shards should be quite large in comparison with the number of nodes to provide the even load distribution. An outage-free shard migration can be done according to the simple protocol that redirects client from the exporting to the importing node during a migration of the shard. The following figure depicts a state machine for get(key) logic as it going to be implemented in Redis Cluster:

It is assumed that each node knows a topology of the cluster and is able to map any key to a shard and a shard to a cluster node. If the node determines that the requested key belongs to a local shard,then it looks it up locally (the upper square in the picture above). If the node determines that the requested key belongs to another node X,than it sends a permanent redirection command to the client (the lower square in the figure above). Permanent redirection means that the client is able to cache the mapping between the shard and the node. If the shard migration is in progress,the exporting and the importing nodes mark this shard accordingly and start to move its records locking each record separately. The exporting node first looks up the key locally and,if not found,redirects the client to the importing node assuming that key is already migrated. This redirect is a one-time and should not be cached. The importing node processes redirects locally,but regular queries are permanently redirected until migration is not completed.

Consistent hashing is basically a mapping schema for key-value store – it maps keys (hashed keys are typically used) to physical nodes. A space of hashed keys is an ordered space of binary strings of a fixed length,so it is quite obvious that each range of keys is assigned to some node as it depicted in the figure (A) for 3 nodes,namely,A,and C. To cope with replication,it is convenient to close a key space into a ring and traverse it clockwise until all replicas are mapped,as it shown in the figure (B). In other words,item Y should be placed on node B because its key corresponds to B’s range,first replica should be placed on C,second replica on A and so on.

This figure depicts a cluster of 16 nodes and illustrates how node A looks up a key that is physically located on node D. Part (A) depicts the route and part (B) depicts partial visions of the ring for nodes A,and C. More information about data replication in decentralized systems can be found in [15].

Each data item is a user account that is attributed by First Name,Last Name,and Phone Number. These attributes are treated as a three-dimensional space and one possible data placement strategy is to map each octant to a dedicated physical node. Queries like “First Name = John” correspond to a plane that intersects 4 octants,hence only 4 nodes should be involved into processing. Queries that restrict two attributes correspond to a line that intersects two octants as it shown in the figure above,hence only 2 nodes should be involved into processing.

This provides more narrowed query-to-nodes mapping,but complicates coordination because one data item becomes scattered across several independent subspaces with their own physical locations and transactional updates become required. More information about this technique and implementation details can be found in [6].

The gray arrows highlight replication of shards from node #2. Shards from the other nodes are replicated symmetrically. The red arrows depict how the passivated replicas will be loaded into RAM in case of failure of node #2. Even distribution of replicas throughout the cluster allows one to have only a small memory reserve that will be used to activate replicas in case of failure. In the figure above,the cluster is able to survive a single node failure having only 1/3 of RAM in reserve. It is worth noting that replica activation (loading from disk to RAM) takes some time and cause temporally performance degradation or outage of the corresponding data during failure recovery.

The scalability requirement can be addressed in significant degree by hierarchically organized monitoring zones that prevent flooding of the network with heartbeat messages [14] and synchronization of different zones via gossip protocol or central fault-tolerant repository. This approach is illustrated below (there are two zones and all six failure detectors talk to each other via gossip protocol or robust repository like ZooKeeper):

Coordinator Election

Coordinator election is an important technique for databases with strict consistency guarantees. First,it allows one to organize failover of a master node in master-slave systems. Second,it allows one to prevent write-write conflicts in case of network partition by terminating partitions that do not include a majority of nodes.

Bully algorithm is a relatively simple approach to coordinator election. MongoDB uses a version of this algorithm to elect leaders in replica sets. The main idea of the bully algorithm is that each member of the cluster can declare itself as a coordinator and announce this claim to other nodes. Other nodes can either accept this claim or reject it by entering the competition for being a coordinator. Node that does not face any further contention becomes a coordinator. Nodes use some attribute to decide who wins and who loses. This attribute can be a static ID or some recency metric like the last transaction ID (the most up-to-date node wins).

An example of the bully algorithm execution is shown in the figure below. Static ID is used as a comparison metric,a node with a greater ID wins.

1. Initially five nodes are in the cluster and node 5 is a globally accepted coordinator.
2. Let us assume that node 5 goes down and nodes 3 and 2 detect this simultaneously. Both nodes start election procedure and send election messages to the nodes with greater IDs.
3. Node 4 kicks out nodes 2 and 3 from the competition by sending OK. Node 3 kicks out node 2.
4. Imagine that node 1 detects failure of 5 now and an election message to the all nodes with greater IDs.
5. Nodes 2,3,and 4 kick out node 1.
6. Node 4 sends an election message to node 5.
7. Node 5 does not respond,so node 4 declares itself as a coordinator and announce this fact to all other peers.

Coordinator election process can count a number of nodes that participate in it and check that at least a half of cluster nodes are attend. This guarantees that only one partition can elect a coordinator in case of network partition.

1. M. Shapiro et al. A Comprehensive Study of Convergent and Commutative Replicated Data Types
2. I. Stoica et al. Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications
3. R. J. Honicky,E.L.Miller. Replication Under Scalable Hashing: A Family of Algorithms for Scalable Decentralized Data Distribution
4. G. Shah. Distributed Data Structures for Peer-to-Peer Systems
5. A. Montresor,Gossip Protocols for Large-Scale Distributed Systems
6. R. Escriva,B. Wong,E.G. Sirer. HyperDex: A Distributed,Searchable Key-Value Store
7. A. Demers et al. Epidemic Algorithms for Replicated Database Maintenance
8. G. DeCandia,et al. Dynamo: Amazon’s Highly Available Key-value Store
9. R. van Resesse et al. Efficient Reconciliation and Flow Control for Anti-Entropy Protocols
10. S. Ranganathan et al. Gossip-Style Failure Detection and Distributed Consensus for Scalable Heterogeneous Clusters
11. http://www.slideshare.net/kakugawa/distributed-counters-in-cassandra-cassandra-summit-2010
12. N. Hayashibara,X. Defago,R. Yared,T. Katayama. The Phi Accrual Failure Detector
13. M.J. Fischer,N.A. Lynch,and M.S. Paterson. Impossibility of Distributed Consensus with One Faulty Process
14. M. Leslie,J. Davies,and T. Huffman. A Comparison Of Replication Strategies for Reliable Decentralised Storage
15. A. Lakshman,P.Malik. Cassandra – A Decentralized Structured Storage System
16. N. A. Lynch. Distributed Algorithms
17. G. Tel. Introduction to Distributed Algorithms
18. http://basho.com/blog/technical/2010/04/05/why-vector-clocks-are-hard/
19. L. Lamport. Paxos Made Simple
20. J. Chase. Distributed Systems,Failures,and Consensus
21. W. Vogels. Eventualy Consistent – Revisited
22. J. C. Corbett et al. Spanner: Google’s Globally-Distributed Database