The FuzzyLog is a partially ordered shared log abstraction. Distributed applications can concurrently append to the partial order and play it back. FuzzyLog applications obtain the benefits of an underlying shared log --- extracting strong consistency, durability, and failure atomicity in simple ways --- without suffering from its drawbacks. By exposing a partial order, the FuzzyLog enables three key capabilities for applications: linear scaling for throughput and capacity (without sacrificing atomicity), weaker consistency guarantees, and tolerance to network partitions. We present Dapple, a distributed implementation of the FuzzyLog abstraction that stores the partial order compactly and supports efficient appends / playback via a new ordering protocol. We implement several data structures and applications over the FuzzyLog, including several map variants as well as a ZooKeeper implementation. Our evaluation shows that these applications are compact, fast, and flexible: they retain the simplicity (100s of lines of code) and strong semantics (durability and failure atomicity) of a shared log design while exploiting the partial order of the FuzzyLog for linear scalability, flexible consistency guarantees (e.g., causal+ consistency), and network partition tolerance. On a 6-node Dapple deployment, our FuzzyLogbased ZooKeeper supports 3M/sec single-key writes, and 150K/sec atomic cross-shard renames.
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Scalog: Seamless Reconfiguration and Total Order in a Scalable Shared Log
The shared log paradigm is at the heart of modern distributed applications in the growing cloud computing industry. Often, application logs must be stored durably for analytics, regulations, or failure recovery, and their smooth operation depends closely on how the log is implemented. Scalog is a new implementation of the shared log abstraction that offers an unprecedented combination of features for continuous smooth delivery of service: Scalog allows applications to customize data placement, supports reconfiguration with no loss in availability, and recovers quickly from failures. At the same time, Scalog provides high throughput and total order. The paper describes the design and implementation of Scalog and presents examples of applications running upon it. To evaluate Scalog at scale, we use a combination of real experiments and emulation. Using 4KB records, a 10 Gbps infrastructure, and SSDs, Scalog can totally order up to 52 million records per second.
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- Award ID(s):
- 1762015
- PAR ID:
- 10192152
- Date Published:
- Journal Name:
- 17th USENIX Symposium on Networked Systems Design and Implementation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The FuzzyLog is a partially ordered shared log abstraction. Distributed applications can concurrently append to the partial order and play it back. FuzzyLog applications obtain the benefits of an underlying shared log - extracting strong consistency, durability, and failure atomicity in simple ways - without suffering from its drawbacks. By exposing a partial order, the FuzzyLog enables three key capabilities for applications: linear scaling for throughput and capacity (without sacrificing atomicity), weaker consistency guarantees, and tolerance to network partitions. We present Dapple, a distributed implementation of the FuzzyLog abstraction that stores the partial order compactly and supports efficient appends / playback via a new ordering protocol. We implement several data structures and applications over the FuzzyLog, including several map variants as well as a ZooKeeper implementation. Our evaluation shows that these applications are compact, fast, and flexible: they retain the simplicity (100s of lines of code) and strong semantics (durability and failure atomicity) of a shared log design while exploiting the partial order of the FuzzyLog for linear scalability, flexible consistency guarantees (e.g., causal+ consistency), and network partition tolerance. On a 6-node Dapple deployment, our FuzzyLog-based ZooKeeper supports 3M/sec single-key writes, and 150K/sec atomic cross-shard renames.more » « less
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