By trying to understand databases in general, I’ve recently dived into the source code of BBoltDB for a deeper understanding of its implementation. It’s a fairly small, nifty local KV store for source-code reading and studying, especially you’re a Golang programmer. At the same time it’s widely used and tested, embeded as the powerful storage engine for systems like Etcd and Consul. Though it’s only backed by one file, it’s capable of serving data size up to TB level.

Below is my notes for the very quick reading from its source code, hopefully providing anyone interested in data systems a quick glimpse of what’s under the hood. It’s not meant as an exhaustive and deep analysis.

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Recently I’ve summarized some experience on quickly getting started with Cassadra. And for this post I’d like to keep writing about some of our experience using and operating Cassandra. Hopefully it could be useful to you, and help you avoid future unwanted surprises.

Election and Paxos

Cassandra is always considered to be favoring the “AP” in “CAP” theorem, where it guarantees eventual consistency for availability and performance. But when really necessary, you can still leverage Cassandra’s built-in “Light-weight Transaction” for elections to determine a leader node in the cluster.

Basically, it works by writing to a table with your own lease:

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INSERT INTO leases (name, owner) VALUES ('lease_master', 'server_1') IF NOT EXISTS;

The IF NOT EXISTS triggers the Cassandra built-in Light-weight Transaction and can be used to declare a consensus among a cluster. With a default TTL in the table, this can be used for leases control, or master election. For example:

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CREATE table leases (
    name text,
    owner text,
) WITH default_time_to_live = 16; 

So that the lease owner needs to keep writing to the lease row for heartbeats.

I’m not sure about the performance characteristics of Cassandra’s election behavior with other applications (etcd, Zookeeper, …) and it’ll be interesting to see a study. But since those are already more full-featured and well-understood in keeping consensus, I’d recommend delegating this behavior to them unless you’re stuck with Cassandra for your application.

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Cassandra Overview

Cassandra as an open-source NoSQL database has gained popularity in cloud and big data applications. Inspired by DynamoDB, it also has good latency, tunable consistency, easy to achieve scalability, and high-availability with cluster setup.

Our team’s been using Cassandra as the backend for an application we’ve been shipping to customers. We chose it for its high-availability setup, and good performance. We used to store time-series data and some simple configuration data as Key-value pairs. So it felt like a natural choice. And in our experience over time, it has proven to be highly capabable at serving our purposes.

With impressive availability, scalability, and read/write performance, Casandra also comes with its limitations. We cannot design data models the same way we did with traditional relational databases with SQL interface. And it doesn’t come with many of the guarantees from traditional databases, like consistency level, transactions, cascading deletion, etc. Like other NoSQL databases, Cassandra was designed to optimize batch write operations with good read and write latency. It fits applications without too much update/delete operations, especially ones with no high amounts of transactions.

So the best use cases for Cassandra can be:

• You have a high volume of data with availability concerns.
• Most data is sequential read/write or append, e.g.: logs, time-series, IoT applications, track records, messages, etc.
• You don’t have complex data relations between data entities that requires high amount of transactions.

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The following is my overly simplified summary of paper reading.

Aurora is a geo-distributed SQL database that supports replication, high-availability, and transactions, with its distributed design around replicating the database WAL log.

References

• Course Syllabus: https://pdos.csail.mit.edu/6.824/schedule.html
• Video Lectures: https://www.youtube.com/channel/UC_7WrbZTCODu1o_kfUMq88g/videos
• Lecture: https://www.youtube.com/watch?v=jJSh54J1s5o

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Reading from Cassandra official website: https://www.datastax.com/sites/default/files/content/whitepaper/files/2019-10/CM2019236 - Data Modeling in Apache Cassandra ™ White Paper-4.pdf

Cassandra is a exemplary implmentation of NoSQL database, and gained popularity in various web, big data, and ML applications. Recently I’ve stumbled upon a good summary of Cassandra handbook, which includes a decent introduction to its data modeling techniques, which can in term be used in other NoSQL databases.

Here are my notes and summaries:

Data Modeling Concepts

There are great many ways Cassandra and traditional RDBMS are different: Cassandra is a wide-column database, with BASE eventual consistency guarantees, has looser relationships between tables. Therefore one needs to model their data very differently than traditional RDBMS for the application to run efficiently.

Namely NoSQL has following differences:

• No Joins: tables have loose relationships with each other without database level joining.
• No Referential Integrity: RDBMS requires foreign keys to refer to primary key in another table. NoSQL doesn’t enforce this.
• Denormalization: contrary to what RDBMS normalization techniques, denormalization is first-class citizen in NoSQL. Many NoSQL databases supports aggregating fields in the same table to achieve row level atomicity.
• Query First: SQL data modeling starts with entities and relations, while NoSQL data modeling starts with application queries.
• Sorting: Sorting is an important design decision, for Cassandra and many NoSQL databases.

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