Database

Introduction I have been reading excellent Database Reliability Engineering book and below are my notes from it. Key Incentive(s) for Automation Elimination of Toil - Toil is the kind of work tied to running a production service that tends to be manual, repetitive, automatable, tactical, devoid of enduring value, and that scales linearly as a service grows. Important System Characteristics Latency, also known as response time, is a time-based measurement indicating how long it takes to receive a response from a request. It is best to measure this for end-to-end response from the customer rather than breaking it down component by component. This is customer-centric design and is crucial for any system that has customers, which is any system Availability - This is generally expressed as a percentage of overall time the system is expected to be available. Availability is defined as the ability to return an expected response to the requesting client. Note that time is not considered here, which is why most SLOs include both response time and availability. After a certain latency point, the system can be considered unavailable even if the request is still completing. Availability is often denoted in percentages, such as 99.9% over a certain window. All samples within that window will be aggregated High availability - For any mission-critical data that you truly care about, you should avoid running with less than three live copies. That’s one primary and two-plus secondaries for leader-follower data stores like MySQL or MongoDB or a replication factor of three for distributed data stores like Cassandra or Hadoop. Because you never, ever want to find yourself in a situation in which you have a single copy of any data you care about, ever. This means that you need to be able to lose one instance while still maintaining redundancy, which is why three is a minimum number of copies, not two. ...

Introduction In a company/enterprise, typically there are multiple sources of data. This could be result of M&A (where each of those add in a new data store) or result of multi year process of using data stores that are in vogue at that time. Result is combination of various types of relational databases, flat file systems, queues and so on. This results in Data Silos. This scenario is typically observed in companies who are running workloads On-prem (i.e. Pre-cloud, Companies who started on Cloud or have moved to it, typically tend to organize data platform better. This could be because of ease of migrating data on cloud. Typically, they centralize it around cheaper object storage (say AWS S3)). ...