Scalable Persistent Storage for Erlang

The many core revolution makes scalability a key property. The RELEASE project aims to improve the scalability of Erlang on emergent commodity architectures with 100,000 cores. Such architectures require scalable and available persistent storage on up to 100 hosts. We enumerate the requirements for scalable and available persistent storage, and evaluate four popular Erlang DBMSs against these requirements. This analysis shows that Mnesia and CouchDB are not suitable persistent storage at our target scale, but Dynamo-like NoSQL DataBase Management Systems (DBMSs) such as Cassandra and Riak potentially are. We investigate the current scalability limits of the Riak 1.1.1 NoSQL DBMS in practice on a 100-node cluster. We establish for the first time scientifically the scalability limit of Riak as 60 nodes on the Kalkyl cluster, thereby confirming developer folklore. We show that resources like memory, disk, and network do not limit the scalability of Riak. By instrumenting Erlang/OTP and Riak libraries we identify a specific Riak functionality that limits scalability. We outline how later releases of Riak are refactored to eliminate the scalability bottlenecks. We conclude that Dynamo-style NoSQL DBMSs provide scalable and available persistent storage for Erlang in general, and for our RELEASE target architecture in particular

CATS: linearizability and partition tolerance in scalable and self-organizing key-value stores

Distributed key-value stores provide scalable, fault-tolerant, and self-organizing storage services, but fall short of guaranteeing linearizable consistency in partially synchronous, lossy, partitionable, and dynamic networks, when data is distributed and replicated automatically by the principle of consistent hashing. This paper introduces consistent quorums as a solution for achieving atomic consistency. We present the design and implementation of CATS, a distributed key-value store which uses consistent quorums to guarantee linearizability and partition tolerance in such adverse and dynamic network conditions. CATS is scalable, elastic, and self-organizing; key properties for modern cloud storage middleware. Our system shows that consistency can be achieved with practical performance and modest throughput overhead (5%) for read-intensive workloads

NewSQL Monitoring System

NewSQL is the new breed of databases that combines the best of RDBMS and NoSQL databases. They provide full ACID compliance like RDBMS and are highly scalable and fault-tolerant similar to NoSQL databases. Thus, NewSQL databases are ideal candidates for supporting big data and applications, particularly financial transaction and fraud detection systems, requiring ACID guarantees. Since NewSQL databases can scale to thousands of nodes, it becomes tedious to monitor the entire cluster and each node. Hence, we are building a NewSQL monitoring system using open-source tools. We will consider VoltDB, a popular open-source NewSQL database, as the database to be monitored. Although a monitoring dashboard exists for VoltDB, it only provides the bird’s eye view of the cluster and the nodes and focuses on CPU usages and security aspects. Therefore, several components of a monitoring system have to be considered and have to be open source to be readily available and congruent with the scalability and fault tolerance of VoltDB. Databases like Cassandra (NoSQL), YugabyteDB (NewSQL), and InfluxDB (Time Series) will be used based on their read/write performances and scalability, fault tolerance to store the monitoring data. We will also consider the role of Amazon Kinesis, a popular queueing, messaging, and streaming engine, since it provides fault-tolerant streaming and batching data pipelines between application and system. This project is implemented using Python and Java

Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approach

This experience report analyses performance of the Cassandra NoSQL database and studies the fundamental trade-off between data consistency and delays in distributed data storages. The primary focus is on investigating the interplay between the Cassandra performance (response time) and its consistency settings. The paper reports the results of the read and write performance benchmarking for a replicated Cassandra cluster, deployed in the Amazon EC2 Cloud. We present quantitative results showing how different consistency settings affect the Cassandra performance under different workloads. One of our main findings is that it is possible to minimize Cassandra delays and still guarantee the strong data consistency by optimal coordination of consistency settings for both read and write requests. Our experiments show that (i) strong consistency costs up to 25% of performance and (ii) the best setting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data consistency under mixed workloads

Dependability Evaluation of Middleware Technology for Large-scale Distributed Caching

Distributed caching systems (e.g., Memcached) are widely used by service providers to satisfy accesses by millions of concurrent clients. Given their large-scale, modern distributed systems rely on a middleware layer to manage caching nodes, to make applications easier to develop, and to apply load balancing and replication strategies. In this work, we performed a dependability evaluation of three popular middleware platforms, namely Twemproxy by Twitter, Mcrouter by Facebook, and Dynomite by Netflix, to assess availability and performance under faults, including failures of Memcached nodes and congestion due to unbalanced workloads and network link bandwidth bottlenecks. We point out the different availability and performance trade-offs achieved by the three platforms, and scenarios in which few faulty components cause cascading failures of the whole distributed system.Comment: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE 2020

Benchmarking Eventually Consistent Distributed Storage Systems

Cloud storage services and NoSQL systems typically offer only "Eventual Consistency", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information

Data modeling with NoSQL : how, when and why

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Analysis of Trade-offs in Fault-Tolerant Distributed Computing and Replicated Databases

This paper examines fundamental trade-offs in fault-tolerant distributed systems and replicated databases built over the Internet. We discuss interplays between consistency, availability, and latency which are in the very nature of globally distributed computer systems and also analyse their interconnection with durability and energy efficiency. In this paper we put forward an idea that consistency, availability, latency, durability and other properties need to be viewed as more continuous than binary in contrast to the well-known CAP/PACELC theorems. We compare different consistency models and highlight the role of the application timeout, replication factor and other settings that essentially determine the interplay between above properties. Our findings may be of interest to software engineers and system architects who develop Internet-scale distributed computer systems and cloud solutions