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PROBABILISTICALLY BOUNDED STALENESS FOR PRACTICAL PARTIAL QUORUMS Brian Hudson PETER BAILIS, SHIVARAM VENKATARAMAN, MICHEAL J. FRANKLIN, JOSEPH M. HELLERSTEIN,

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Presentation on theme: "PROBABILISTICALLY BOUNDED STALENESS FOR PRACTICAL PARTIAL QUORUMS Brian Hudson PETER BAILIS, SHIVARAM VENKATARAMAN, MICHEAL J. FRANKLIN, JOSEPH M. HELLERSTEIN,"— Presentation transcript:

1 PROBABILISTICALLY BOUNDED STALENESS FOR PRACTICAL PARTIAL QUORUMS Brian Hudson PETER BAILIS, SHIVARAM VENKATARAMAN, MICHEAL J. FRANKLIN, JOSEPH M. HELLERSTEIN, ION STOICA Data store replication results in a fundamental trade-off between operation latency and data consistency. In this paper, we examine this trade-off in the context of quorum-replicated data stores. Under partial, or non-strict quorum replication, a data store waits for responses from a subset of replicas before answering a query, without guaranteeing that read and write replica sets intersect. As deployed in practice, these configurations provide only basic eventual consistency guarantees, with no limit to the recency of data returned. However, anecdotally, partial quorums are often “good enough” for practitioners given their latency benefits. In this work, we explain why partial quorums are regularly acceptable in practice, analyzing both the staleness of data they return and the latency benefits they offer. We introduce Probabilistically Bounded Staleness (PBS) consistency, which provides expected bounds on staleness with respect to both versions and wall clock time. We derive a closed-form solution for versioned staleness as well as model real-time staleness for representative Dynamo-style systems under internet-scale production workloads. Using PBS, we measure the latency-consistency trade-off for partial quorum systems. We quantitatively demonstrate how eventually consistent systems frequently return consistent data within tens of milliseconds while offering significant latency benefits. CS 644

2 Paper Selection  Did NOT propose yet another new consistency model or develop a system implementing new semantics  Instead it examines what level of consistency existing quorum-replicated systems actually provide.  Amazon Dynamo  Basho Riak  Project Voldemort

3 Background  Modern distributed data stores need to be scalable, highly available, and fast.  These systems often replicate data across different machines and often across datacenters for two reasons:  Provide high availability when components fail  Provide improved performance by serving requests from multiple replicas

4 Background  To provide low read and write latency, these systems often forgo guaranteeing consistency of reads and instead opt for eventual consistency.  BUT – these “eventually consistent” systems make no guarantees on the staleness (recency in terms of version written) beyond simply saying that the system will “eventually” return the most recent version in the absence of new writes.  Probabilistically Bounded Staleness (PBS) aims to fill this gap, providing SLA-style consistency predictions.  Time: “How eventual?”  Version history: “How consistent?”  A way to quantify latency-consistency trade-offs

5 Background – Quorum Systems  Quorum systems ensure strong consistency across reads and writes to replicas by ensuring that read and write replica sets overlap/intersect.  R + W ≥ N  Partial quorum systems lower latency by requiring fewer replicas to respond.  Sets of replicas written to and read from need not overlap: R + W ≤ N N replicas/key Read: wait for R replies Write: wait for W acks R = read quorum sizes W = write quorum sizes N = number of replicas N = 3, W = 2

6 Background – Partial Quorums  Probabilistic Quorum Systems  Provides probabilistic guarantees of quorum intersection  By scaling the number of replicas, an arbitrarily high probability of consistency can be achieved

7 PBS: k-staleness  k-staleness  Models the probability that we will read a value that is no more than k versions older than the last written version.  How consistent is eventual consistency? A quorum system obeys PBS k-staleness consistency if, with probability 1 – p sk, at least one value in any read quorum has been committed within k versions of the latest committed version when the read begins.

8 PBS: k-staleness  Simple closed form solution  N = 3, R = W = 1  Probability of returning a version Within 2 versions is 0.5 Within 3 versions is Within 5 versions > Within 10 versions > 0.98 Number of quorums of size R composed of nodes that were not written to in the write quorum divided by the number of possible read quorums

9 PBS: k-staleness  N = 3, R = W = 1  Probability of returning a version Within 2 versions is 0.5 Within 3 versions is Within 5 versions > Within 10 versions > 0.98

10 PBS: t-staleness  t-visibility  Models the probability of inconsistency for expanding quorums. t-visibility is the probability that a read operation, starting t seconds after a write commits, will observe the latest value of a data item.  This t captures the expected length of the “window of consistency”.  How eventual is eventual consistency?

11 PBS: t-staleness  The above equation makes several assumptions  Reads occur instantly  Writes commit immediately after W replicas have the version  Not realistic! T-staleness in real systems depend on write latency and propagation speeds

12 PBS: t-staleness  WARS Model  Describes the message latencies between coordinator and a single replica for a write followed by a read t seconds after commit. In an N replica system, this messaging occurs N times.

13 PBS: t-staleness  Given a distribution for each WARS, calculating t- visibility for a given value of t is straightforward  Implemented a Monte Carlo simulation using the WARS model for computing t-staleness  The simulation runs quickly and web version can be found here:  Modified Cassandra, instrumented it to record information needed to form WARS distributions  Source code for patch is available online  WARS predictions closely matched empirical observations, validating the simulation

14 PBS: Production Latency Distributions  Obtained real-world latency information from LinkedIn on a Voldemort distribution running on traditional disks and one on SSDs.  Single node under peak traffic, representing 60% read and 40% read-modify-write traffic  Big improvement with SSDs! Voldemort goes from being IO bound when running on spinning drives to being CPU or network bound when running on SSDs.  Probably why Amazon DynamoDB runs on SSDs

15 PBS: Production Latency Distributions  Yammer provides private social networking for companies.  Uses Riak

16 PBS Latency Model Fitting  Provided data (summary statistics) was under-specified. WARS requires distributions.  Authors massaged this data into WARS distributions  NOTE: WARS distribution information can be easily collected (as their Cassandra patch shows), it just currently is not collected in practice

17 PBS  Maintaining a large number of replicas for availability or better performance results in a potentially large impact on consistency immediately following a write, but it still converges quickly.  Write latency has a huge impact: compare SSD to disk  SSD write latency median.489ms  Disk write latency median 1.5ms  SSD has 97.4% chance of consistent reads immediately after write  Disk has 43.9% chance of consistent reads immediately after write  Data suggests SSDs can make a HUGE impact on consistency due to reduce write latency and write variance.  Probably why Amazon DynamoDB is built using only SSDs

18 PBS  Increasing N while keeping R and W constant  Maintaining a large number of replicas for availability or better performance results in a potentially large impact on consistency immediately following a write, but it still converges quickly.

19 Cassandra Defaults

20 Riak Defaults

21 Riak “Low Value” Data Recommendation


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