Skip to content
Permalink
Branch: master
Find file Copy path
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
195 lines (151 sloc) 9.77 KB

Building Feature-Rich Data Stores Using a Central Log

This document was written in January 2019 as an extended abstract for an internal conference. Some sections were redacted and edited before making this public.

Consistent and fault-tolerant data stores are commonly built using one of two approaches:

  1. The "RSM" approach uses one replicated state machine [1, 2] or one primary-backup group. The entire dataset is replicated to every machine. Examples include ZooKeeper [3], PostgreSQL [4], and Neo4j [5], and Stardog [6]. This approach is simple to implement and can optimize for read queries using local data structures. Such systems are often feature-rich. They can scale in read throughput by having replicas serve requests, but their write throughput and capacity is fundamentally limited.

  2. The "stamped-out" approach is to apply the RSM approach many times. Each RSM serves as an independent unit, hosting one partition or shard. Examples include Kafka [8], RAMCloud [9], and CockroachDB [10]. Such systems scale in read throughput, write throughput, and capacity. However, any operation involving multiple partitions is complicated by communication and coordination, and query performance depends greatly on the partitioning scheme used.

At the end of 2017, we set out to build a graph store with more capacity than the RSM approach could offer. However, we weren't ready to accept the complexity of the stamped-out approach or to limit the cross-partition features in our store -- graphs are notoriously difficult to partition. We hoped to find some middle ground.

The key problem was finding a suitable architecture to implement a data store with the following properties:

  • Fault-tolerant (no single point of failure),
  • Consistent (not eventually-consistent),
  • Scalable capacity and query rates (10 to 100x that of a single node),
  • Modest write rates (10K-100K writes per second), and
  • Supporting hard-to-partition queries (such as SQL, graphs).

We explored this problem through our Beam prototypes. We first built an in-memory key-value store from scratch [11], then iterated to a disk-based property graph [12], then to a knowledge graph [13].

We based our implementations around a central log. The central log isn't a novel idea [14, 15], but it's often overlooked. The central log was the key to enabling us to iterate quickly in a small team. We describe our experience and compare with the stamped-out approach, which we've had experience implementing in the past [9]. We argue that others should use a central log to build data stores with similar requirements.

The central log architecture [11] is shown in the figure. All write requests are sequenced into an append-only central log. The log is a network service that is internally replicated for fault-tolerance and persisted for durability. Several view servers read the log and apply its entries in sequence, each deterministically updating its local state. Different view servers maintain different state. An API tier accepts requests from clients. It appends the write requests to the log, and it collects data from the view servers to answer reads.

architecture

The central log introduces one constraint and one complication. The constraint is that it imposes a fundamental bottleneck: the maximum rate of appends to the log determines the maximum rate of change to the entire dataset [11]. Most log implementations can achieve at least tens of thousands of appends per second. The complication is that older entries of the log must be truncated eventually, to prevent the log from growing without bound. Some view servers must retain and serve copies of that information [17].

In exchange, we've found many features to be drastically easier to implement using a central log than with the stamped-out approach (we implemented all of these in Beam):

  • Transactions with a stamped-out approach require complex distributed protocols [18, 19]. Using a central log, there's no need for coordination outside the log [11, 14, 20, 21].

  • Consistent cross-partition queries and historical global snapshots were easy to implement with a central log, because a log index identifies a global version of the dataset. In the stamped-out approach, these require either distributed transactions [18], relaxing consistency [22], or atomic clocks [23].

  • Replication, data migration, and cluster membership were easier to implement using a central log, because no server strongly "owns" data in that model. View servers receive all updates through the log. They only need to replicate snapshots to each other during boot (to enable log compaction) [17]. In contrast, stamped-out systems must coordinate ownership handoffs and membership changes using complex protocols [9].

  • Partitioning and indexing the dataset multiple ways were both easier to implement using a central log by adding view servers. In a stamped-out system, doing so requires updating the scope of every transaction.

We believe the central log is well-suited to meeting the requirements stated above. Using a cluster of small VMs (40 GB disk, 8 GB RAM, 4 VCPUs), our Beam implementation stored 4 billion items totalling 1.2 TB of data. Using Kafka, the data was loaded at an average write rate of 25 MB/sec [12].

We urge others to consider our experience when building future data stores. The central log is a great approach for systems that need more capacity than an RSM can provide but don't need infinitely-scalable write throughput. Not only is it simpler than the stamped-out approach, it's better suited for implementing features like transactions and special-purpose indexes. These low-level features enable expressive data models and query languages, with easy to understand consistency properties, for clients.

References

  1. Schneider, F B. Implementing fault-tolerant services using the state machine approach: a tutorial. ACM Computing Surveys 22, 4 (Dec. 1990).

  2. Ongaro, D; Ousterhout, J. In Search of an Understandable Consensus Algorithm. USENIX Annual Technical Conference (2014). PDF

  3. Hunt, P; Konar, M; Junqueira, F P; Reed, B. ZooKeeper: wait-free coordination for internet-scale systems. USENIX Annual Technical Conference (2010).

  4. PostgreSQL Global Development Group, The. PostgreSQL 11.1 Documentation: High Availability, Load Balancing, and Replication. https://www.postgresql.org/docs/11/high-availability.html

  5. Neo4j, Inc. The Neo4j Operations Manual v3.5: Introduction to New4j Causal Clustering. https://neo4j.com/docs/operations-manual/current/clustering/introduction/

  6. Stardog Union. Stardog 6: The Manual: High Availability Cluster. https://www.stardog.com/docs/#_high_availability_cluster

  7. [redacted]

  8. Apache Software Foundation. Kafka 2.1 Documentation: Design: Replication. https://kafka.apache.org/documentation/#replication

  9. Ousterhout, J; Gopalan, A; Gupta, A; Kejriwal, A; Lee, C; Montazeri, B; Ongaro, D; Park, S J; Qin, H; Rosenblum, M; Rumble, S M; Stutsman, R; Yang, S. The RAMCloud Storage System. ACM Transactions on Computer Systems (Sept. 2015). PDF

  10. Cockroach Labs. CockroachDB Documentation: Concepts: Architecture Overview. https://www.cockroachlabs.com/docs/stable/architecture/overview.html

  11. ProtoBeam v1 (Nov. 2017).

  12. ProtoBeam v2 (Mar. 2018).

  13. ProtoBeam v3 (Aug. 2018).

  14. Balakrishnan, M; Malkhi, D; Wobber, T; et al. Tango: Distributed Data Structures over a Shared Log. Symposium on Operating Systems Principles (2013). PDF

  15. Kreps, J. The Log: What every software engineer should know about real-time data's unifying abstraction. LinkedIn Engineering Blog (2013). https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying

  16. [redacted]

  17. Booting Beam Views (Mar. 2018).

  18. Lee, C; Park, S J; Kejriwal, A; Matsushita, S; Ousterhout, J. Implementing linearizability at large scale and low latency. Symposium on Operating Systems Principles (2015). ACM Open Access

  19. Tracy, M. How CockroachDB Does Distributed, Atomic Transactions. Cockroach Labs Blog (Sep. 2015). https://www.cockroachlabs.com/blog/how-cockroachdb-distributes-atomic-transactions/

  20. Thomson, A; Diamond, T; Weng, S-C; Ren, K; Shao, P; Abadi, D J. Calvin: Fast Distributed Transactions for Partitioned Database Systems. ACM SIGMOD International Conference on Management of Data (2012). PDF

  21. Freels, M. Consistency without Clocks: The FaunaDB Distributed Transaction Protocol. Fauna Blog (Oct. 2018). https://fauna.com/blog/consistency-without-clocks-faunadb-transaction-protocol

  22. Du, J; Elnikety, S; Zwaenepoel, W. 2013. Clock-SI: Snapshot Isolation for Partitioned Data Stores Using Loosely Synchronized Clocks. IEEE Symposium on Reliable Distributed Systems (2013). PDF

  23. Corbett, J C; Dean, J; Epstein, M; et al. Spanner: Google’s Globally-Distributed Database. USENIX Symposium on Operating Systems Design and Implementation (2012). PDF

You can’t perform that action at this time.