Non-Monotonic Snapshot Isolation

Many distributed applications require transactions. However, transactional protocols that require strong synchronization are costly in large scale environments. Two properties help with scalability of a transactional system: genuine partial replication (GPR), which leverages the intrinsic parallelism of a workload, and snapshot isolation (SI), which decreases the need for synchronization. We show that, under standard assumptions (data store accesses are not known in advance, and transactions may access arbitrary objects in the data store), it is impossible to have both SI and GPR. To circumvent this impossibility, we propose a weaker consistency criterion, called Non-monotonic Snapshot Isolation (NMSI). NMSI retains the most important properties of SI, i.e., read-only transactions always commit, and two write-conflicting updates do not both commit. We present a GPR protocol that ensures NMSI, and has lower message cost (i.e., it contacts fewer replicas and/or commits faster) than previous approaches.Cet article étudie deux propriétés favorisant le passage à l'échelle des systèmes répartis transactionnels: la réplication partielle authentique GPR), et le critère de cohérence Snapshot Isolation (SI). GPR spécifie que pour valider une transaction T, seules les répliques des données accédées par T effectuent des pas de calcul. SI définit que toute transaction doit lire une vue cohérente du système, et que deux transactions concurrentes ne peuvent écrire la même donnée. Nous montrons que ces deux propriétés sont incompatibles. Nous proposons ensuite un nouveau critère de cohérence nommé Non-Monotonic Snapshot Isolation (NMSI). NMSI est proche de SI et compatible avec GPR. Pour justifier ce propos, nous présentons un protocole authentique implémentant de manière efficace NMSI. Au regard des travaux précédents sur le contrôle de concurrence dans les systèmes répartis transactionnelles, notre protocole est plus performant en latence et/ou en nombre de messages échangés

MDCC: Multi-Data Center Consistency

Replicating data across multiple data centers not only allows moving the data closer to the user and, thus, reduces latency for applications, but also increases the availability in the event of a data center failure. Therefore, it is not surprising that companies like Google, Yahoo, and Netflix already replicate user data across geographically different regions. However, replication across data centers is expensive. Inter-data center network delays are in the hundreds of milliseconds and vary significantly. Synchronous wide-area replication is therefore considered to be unfeasible with strong consistency and current solutions either settle for asynchronous replication which implies the risk of losing data in the event of failures, restrict consistency to small partitions, or give up consistency entirely. With MDCC (Multi-Data Center Consistency), we describe the first optimistic commit protocol, that does not require a master or partitioning, and is strongly consistent at a cost similar to eventually consistent protocols. MDCC can commit transactions in a single round-trip across data centers in the normal operational case. We further propose a new programming model which empowers the application developer to handle longer and unpredictable latencies caused by inter-data center communication. Our evaluation using the TPC-W benchmark with MDCC deployed across 5 geographically diverse data centers shows that MDCC is able to achieve throughput and latency similar to eventually consistent quorum protocols and that MDCC is able to sustain a data center outage without a significant impact on response times while guaranteeing strong consistency

Non-Monotonic Snapshot Isolation: scalable and strong consistency for geo-replicated transactional systems

International audienceModern cloud systems are geo-replicated to improve application latency and availability. Transactional consistency is essential for application developers; however, the corresponding concurrency control and commitment protocols are costly in a geo-replicated setting. To minimize this cost, we identify the following essential scalability properties: (i) only replicas updated by a transaction T make steps to execute T; (ii) a read-only transaction never waits for concurrent transactions and always commits; (iii) a transaction may read object versions committed after it started; and (iv) two transactions synchronize with each other only if their writes conflict. We present Non-Monotonic Snapshot Isolation (NMSI), the first strong consistency criterion to allow implementations with all four properties. We also present a practical implementation of NMSI called Jessy, which we compare experimentally against a number of well-known criteria. Our measurements show that the latency and throughput of NMSI are comparable to the weakest criterion, read-committed, and between two to fourteen times faster than well-known strong consistencies

Consistency in 3D

Comparisons of different consistency models often try to place them in a linear strong-to-weak order. However this view is clearly inadequate, since it is well known, for instance, that Snapshot Isolation and Serialisability are incomparable. In the interest of a better understanding, we propose a new classification, along three dimensions, related to: a total order of writes, a causal order of reads, and transactional composition of multiple operations. A model may be stronger than another on one dimension and weaker on another. We believe that this new classification scheme is both scientifically sound and has good explicative value. The current paper presents the three-dimensional design space intuitively.Les comparaisons entre modèles de la cohérence tentent souvent de les classer dans un ordre linéaire, de faible à forte. Cette vue est clairement inadéquate, puisque il est bien connu que, par exemple, les modèles Snapshot Isolation et Serialisability sont incomparables. Dans l'intérêt d'une meilleure compréhension du domaine, nous proposons une nouvelle classification, en trois dimensions~: les garanties liées à un ordre total des écritures~; celles liées à un ordre causal des lectures~; et celles liées à la composition transactionelle d'opérations multiples. Un modèle peut être plus fort qu'un autre dans une dimension, et moins dans une autre. Nous pensons que ce nouveau schéma de classification, à la fois est scientifiquement valide, et a une bonne valeur explicative. Le présent rapport présente l'espace de conception en trois dimensions de façon intuitive

Una implementación rápida de Parallel Snapshot Isolation

Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2019/2020.Most distributed database systems offer weak consistency models in order to avoid the performance penalty of coordinating replicas. Ideally, distributed databases would offer strong consistency models, like serialisability, since they make it easy to verify application invariants, and free programmers from worrying about concurrency. However, implementing and scaling systems with strong consistency is difficult, since it usually requires global communication. Weak models, while easier to scale, impose on the programmers the need to reason about possible anomalies, and the need to implement conflict resolution mechanisms in application code. Recently proposed consistency models, like Parallel Snapshot Isolation (PSI) and NonMonotonic Snapshot Isolation (NMSI), represent the strongest models that still allow to build scalable systems without global communication. They allow comparable performance to previous, weaker models, as well as similar abort rates. However, both models still provide weaker guarantees than serialisability, and may prove difficult to use in applications. This work shows an approach to bridge the gap between PSI, NMSI and strong consistency models like serialisability. It introduces and implements fastPSI, a consistency protocol that allows the user to selectively enforce serialisability for certain executions, while retaining the scalability properties of weaker consistency models like PSI and NMSI. In addition, it features a comprehensive evaluation of fastPSI in comparison with other consistency protocols, both weak and strong, showing that fastPSI offers better performance than serialisability, while retaining the scalability of weaker protocols.La mayoría de las bases de datos distribuidas ofrecen modelos de consistencia débil, con la finalidad de evitar la penalización de rendimiento que supone la coordinación de las distintas réplicas. Idealmente, las bases de datos distribuidas ofrecerían modelos de consistencia fuerte, como serialisability, ya que facilitan la verificación de los invariantes de las aplicaciones, y permiten que los programadores no deban preocuparse sobre posibles problemas de concurrencia. Sin embargo, implementar sistemas escalables que con modelos de consistencia fuerte no es fácil, pues requieren el uso de comunicación global. Sin embargo, aunque los modelos de consistencia más débiles permiten sistemas más escalables, imponen en los programadores la necesidad de razonar sobre posibles anomalías, así como implementar mecanismos de resolución de conflictos en el código de las aplicaciones. Dos modelos de consistencia propuestos recientemente, Parallel Snapshot Isolation (PSI) y Non-Monotonic Snapshot Isolation (NMSI), representan los modelos más fuertes que permiten implementaciones escalables sin necesidad de comunicación global. Permiten, a su vez, implementar sistemas con rendimientos similares a aquellos con modelos más débiles, a la vez que mantienen tasas de cancelación de transacciones similares. Aun así, ambos modelos no logran ofrecer las mismas garantías que serialisability, por lo que pueden ser difíciles de usar desde el punto de vista de las aplicaciones. Este trabajo presenta una propuesta que busca acortar la distancia entre modelos como PSI y NMSI y modelos fuertes como serialisability. Con esa finalidad, este trabajo presenta fastPSI, un protocolo de consistencia que permite al usuario ejecutar de manera selectiva transacciones serializables, reteniendo a su vez las propiedades de escalabilidad propias de modelos de consistencia débiles como PSI o NMSI. Además, este trabajo cuenta con una evaluación exhaustiva de fastPSI, comparándolo con otros protocolos de consistencia, tanto fuertes como débiles. Se muestra así que fastPSI logra un rendimiento mayor que serialisability sin por ello renunciar a la escalabilidad de protocolos más débiles.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Dynamic Package Interfaces - Extended Version

A hallmark of object-oriented programming is the ability to perform computation through a set of interacting objects. A common manifestation of this style is the notion of a package, which groups a set of commonly used classes together. A challenge in using a package is to ensure that a client follows the implicit protocol of the package when calling its methods. Violations of the protocol can cause a runtime error or latent invariant violations. These protocols can extend across different, potentially unboundedly many, objects, and are specified informally in the documentation. As a result, ensuring that a client does not violate the protocol is hard. We introduce dynamic package interfaces (DPI), a formalism to explicitly capture the protocol of a package. The DPI of a package is a finite set of rules that together specify how any set of interacting objects of the package can evolve through method calls and under what conditions an error can happen. We have developed a dynamic tool that automatically computes an approximation of the DPI of a package, given a set of abstraction predicates. A key property of DPI is that the unbounded number of configurations of objects of a package are summarized finitely in an abstract domain. This uses the observation that many packages behave monotonically: the semantics of a method call over a configuration does not essentially change if more objects are added to the configuration. We have exploited monotonicity and have devised heuristics to obtain succinct yet general DPIs. We have used our tool to compute DPIs for several commonly used Java packages with complex protocols, such as JDBC, HashSet, and ArrayList.Comment: The only changes compared to v1 are improvements to the Abstract and Introductio