Paper

I read the Google Spanner paper, Spanner: Google’s Globally-Distributed Database, so I’m going to share my take on it. We’ll focus on its essence rather than covering every detail, so we can get an overview of what Spanner is, what makes it powerful, and how it works. Google Spanner TL;DR Google already had Bigtable and Megastore, but both had limitations. Bigtable was difficult to use for applications requiring complex schemas or strong consistency across datacenters. Megastore provided a semi-relational data model and synchronous replication, but its write throughput was relatively low. Spanner combines automatic sharding, synchronous replication, serializable distributed transactions, and SQL-based queries in a globally distributed database. One of its key features is external consistency: the order of transactions respects the order in which they happen in the real world. To achieve this, Spanner uses TrueTime, a novel clock API that exposes bounded clock uncertainty. TrueTime also helps Spanner support lock-free read-only transactions and globally consistent snapshot reads. Architecture Although Spanner supports SQL-like queries, its underlying storage is more like a distributed key-value store. Data is stored as versioned key-value pairs. Let’s look at how Spanner organizes and replicates this data. ...

In this post, we’ll walk through how Amazon Aurora Limitless works under the hood, based on the paper Aurora PostgreSQL Limitless Database: Building a Highly Scalable OLTP Database. Rather than covering every detail of the paper, I’ll focus on what I found most interesting: how Limitless implements MVCC across a distributed database. Amazon Aurora Limitless TL;DR How can we horizontally scale Aurora? Limitless answers this question by sharding data across multiple database instances. A Limitless database consists of routers and shards. Routers receive client queries, build query plans, and distribute work among shards. Each shard stores and processes a portion of the data. Conventional XID-based MVCC does not scale naturally to a distributed database because it requires global transaction information. Limitless instead implements time-based MVCC using Amazon Time Sync Service and Hybrid Logical Clocks (HLCs). Limitless can scale vertically by increasing the capacity of individual routers and shards, and horizontally by adding more of them. Routers and Shards 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Client | v +-------------------+ | DNS Endpoint | +-------------------+ / \ v v +----------+ +----------+ | Router 1 | | Router 2 | +----------+ +----------+ | | +-------+-------+ | +------------+------------+ | | | v v v +---------+ +---------+ +---------+ | Shard 1 | | Shard 2 | | Shard 3 | +---------+ +---------+ +---------+ | | | v v v +---------+ +---------+ +---------+ | Storage | | Storage | | Storage | | Volume | | Volume | | Volume | +---------+ +---------+ +---------+ In Limitless, customers can specify a sharding key for a table. The table is divided into partitions using hash-based partitioning, and those partitions are distributed across shards. Typically, a sharded table is divided into 256 partitions. ...

In this post, we’ll walk through how Amazon Aurora works under the hood, based on the paper Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases. Amazon Aurora TL;DR In the cloud era, OLTP databases need both high performance and resilience to failures. Aurora pushes the separation of compute and storage further by introducing a distributed storage service responsible for redo processing and page materialization. Aurora addresses the network I/O bottleneck between compute and storage by sending only redo logs describing changes, instead of writing full pages over the network. How Aurora Achieves Durability In a distributed environment, failures are always happening somewhere, ranging from temporary issues such as slow disk reads or network congestion to permanent failures of disks, nodes, or even an entire data center. A cloud database needs to remain available despite these failures. ...