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Replicated Distributed Databases for Amazon

1680 words (7 pages) Essay in Information Systems

08/02/20 Information Systems Reference this

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Introduction

Amazon runs its e-commerce operation around the world. For this, Amazon uses a highly decentralized, loosely coupled and service-oriented architecture, containing hundreds of services. Amazon in the year 2004, was growing at its peak, and was starting to hit the maximum scaling limits of its Oracle database, to overcome this limitations of their RDBMS, engineers started looking at their current database schema, and they identified some of the major issues with RDBMS and found that 90 percent of the SQL operations were not using JOIN functionality which are the core of the relational databases. Engineers concluded that not every application needs the datastore to be ACID compliant, and relaxing consistency can improve the availability. In addition, the replication technologies available with RDBMS are limited and typically choose consistency over availability, also It is not easy to scale-out RDBMS or use smart partitioning schemes for load balancing. For overcoming these limitations of relational database systems, Amazon has designed Dynamo, which is an eventually consistent storage system and can be used in production with demanding applications. The paper describes the learnings from building a highly available key-value store designed to meet the growth of Amazon.com.

Assumptions & Intended Usage

With millions of components in Amazon’s architecture, there are always number of network components failing in normal operation. In general, Amazon’s Software services are designed with consideration to treat failure as normal case w/o impacting performance or availability. Dynamo was intended to meet the need of high availability and scalability required of the Amazon’s platform.

With diverse sets of applications ranging from shopping cart to complex security requirements, it was observed that majority of the application only need simple primary key based access to the data store. Also, experimental analysis at Amazon has proven that applications relying on data stores satisfying transactional ACID properties failed to address the high availability need of Amazon platform. Dynamo targets the needs of application that need high availability at the expense of consistency. Also, Dynamo does not guarantee isolation (‘I’ in ACID properties). Instead it only allows single key update in its data store. Instead of supporting complex relational queries like joins, Dynamo’s design was based on the assumption that services can work with majority of their operations spread across single data item.

Another assumption was that Amazon’s platform services incorporating Dynamo should be able to meet the stringent latency and throughput requirements. Additionally, to control the infrastructure cost, it was a necessity to run the system on easily affordable hardware. To provide this ability, applications can configure dynamo to balance the tradeoff between performance, availability, and cost. Also, Dynamo’s design was established on the assumption that each service should run in a non-hostile environment with its own instance and no additional security requirements.

Design tradeoffs

In a distributed system, when data is stored in a particular node, it has to be updated to other backup nodes. Here, a node is a data center. Database replication improves reliability of the system by providing backups in cases of failures [1]. However, in a distributed environment facing frequent network failures, maintaining consistency and availability becomes cumbersome. As per the CAP theorem, in a distributed system, a system can only achieve two of the three desirable properties i.e. consistency, availability, and partition tolerance [2]. For an application such as Amazon’s platform, which serves to an increasing amount of user traffic, it was a requirement to provide availability and reliability over consistency.

The tradeoff of consistency meant that the updated data would not be present across multiple data nodes at a particular moment. This resulted in a system that required conflict resolution. Dynamo, an “always writable” data store, had to provide conflict resolution via application level or data store itself. Conflict resolution via application was advantageous as the developer knew about the data schema and could decide on a proper resolution approach. However, in case of conflict resolution via data store, it employed a simple policy of “last write wins”.

With the acceptable consistency tradeoff in the system design, Dynamo can achieve horizontal scaling. In horizontal scaling, a system has multiple nodes in a network and it can cater to the increasing requests. Dynamo’s support for incremental scalability helps system to scale out one node at a time. To achieve this, Dynamo employs consistent hashing technique for spreading the data items across multiple nodes. This technique does not affect the performance of the system.

Amazon makes use of service-oriented architecture where each business unit has a separate service. This approach was employed by Amazon to cater to the incoming requests without frequent alterations to the database schema. For services that demanded high performance, Dynamo was able to trade-off durability. To attain this, the write operations were written to buffer initially and then periodically moved to storage engine. Moreover, the read operations checked buffer for the key initially and if not found then were read from storage engine.

 

Implementation

In order to meet the requirement of high scalability, Dynamo uses a modified Consistent Hashing technique in which it partitions the data across multiple nodes. Instead of mapping a node to a single point in a ring, the nodes are mapped to more than one points called as virtual nodes. This ensures that there is a uniform distribution of data across all the nodes and also improves performance. To achieve high availability each data item is replicated at N different nodes. Dynamo uses vector clocks to maintain different versions of the same object. Each vector clock consists of a (node, context) pair. Each modification to data results in an immutable version of the data. Dynamo also stores a timestamp indicating the last time the node updated the object. To maintain consistency among all the replicas, Dynamo uses consistency protocol similar to quorum systems. It maintains a list of top N nodes in a preference list for any given key. It sets R and W such that R + W > N, where R and W is the number of nodes that should participate in successful read operations and write operation. To recover from temporary node failures Dynamo uses sloppy quorum technique where all the read and write operations are performed by the top N healthy nodes. To recover from permanent node failures Dynamo uses anti-entropy protocol to minimize the inconsistencies between the data by constructing a Merkle Tree. A gossip-based protocol is used to add or remove a node from the Dynamo ring.

Every node consists of three main software components: request coordination, membership and failure detection and a local persistence engine. It supports multiple storage engines like MySQL, Berkeley Database (BDB) etc. The purpose of having multiple storage engine is to choose the one which is best suited for an application. On top of this Dynamo has a request coordination system which coordinates read and write operation for a given key on behalf of the client. Each client request creates a new state machine on the node that is processing the request. The state machine has logic for forwarding the request to corresponding node, doing multiple retries, receiving response from the node and sending the response back to the client.

 

Advantages

The ease of scaling a large number of nodes in and out ensuring high availability is the main advantage. Dynamo is fully managed system with automatic scaling, where the nodes can be added or removed automatically. All the nodes in the system are utilized to their optimum capacity due to which the efficiency of resource usage is high. The SPOC (single point of Contact) is avoided and a single node failure does not affect the entire system. It is also very easy to integrate with AWS Lambda and API Gateway.

 

Disadvantages

The main disadvantage is that Dynamo is not ACID compliant, i.e. it does not support Atomicity and Consistency. It becomes expensive for storing large size data, for example, a large JSON blob (1MB file size limit on querying). Once we hit the read (or write) limit, further requests are denied until enough time is elapsed. Dynamo needs some scripts to back up the tables in. There are considerable overheads, as compared to SQL. Joins are impossible and all complex data relations needs to be managed at the code layer. Lack of support of Foreign keys and triggers also add to the overheads. Also, there is some Latency Availability, where a table created is not available instantly.

Learning and Criticism

Dynamo is a state-of-the-art technique to create databases which in turn led to the emergence of other NoSQL databases. It is highly configurable with functions like read-only and write-only and techniques like Consistent Hashing and Merkle tree have been highlighted in the paper.

There are some disadvantages though which were not clearly stated or were lacking substantial quantitative evidence to support the claims. For example, although high scalability is stated as advantage, there are no claims or explanation on the performance when operating an exponentially large number of nodes. The paper presents admin monitored (command line based) mechanism to handle addition or removal of node from Dynamo ring. However, with hundreds of nodes and multiple failures, there could be too much human intervention. This could lead to time consuming, inefficient membership management.

 

References:

 [1] Bernstein, P.A., and Goodman, N. An algorithm for concurrency control and recovery in replicated distributed databases. ACM Trans. on Database Systems, 9(4):596-615, December 1984

[2] S. S. Shim, “Guest Editor’s Introduction: The CAP Theorem’s Growing Impact,” in Computer, vol. 45, no., pp. 21-22, 2012. doi:10.1109/MC.2012.54 keywords: {CAP theorem; distributed databases}, url: doi.ieeecomputersociety.org/10.1109/MC.2012.54

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