Jethro's Braindump

Designing Data-Intensive Applications

tags
System Design, Databases, Hadoop
author
Martin Kleppmann

Preface

  • Data abstractions are well-built (databases, etc.) but the boundaries are blurring
  • Design decisions are highly centered around data requirements (both functional and non-functional requirements)
  • How to build reliable, scalable, and maintainable applications

Data Models

  • Prevalence of SQL
  • NoSQL has benefits of being schemaless (lower impedence mismatch) and storage locality, when fetching whole documents
    • joins are poorly implemented, can’t access nested item in document efficiently
  • Graph DBs implement many-to-many relationships well, also somewhat schemaless

Database Implementation

SSTable

SSTables keep key-value pairs sorted by key, which allows efficient key-value lookups and range queries. They use a log-structure indexes to break the database down into segments, and always writes segments sequentially.

  • When a write comes in, add it into an in-memory balanced data structure (e.g. Red-Black Tree). This is sometimes referred to as a memtable.
  • When the memtable becomes too big, write it out onto disk as an SSTable file. The SSTable file becomes the most recent segment of the database.
  • In order to serve a read request, the key is searched for in the memtable, then in the most recent on-disk segment, and so-on.
  • From time to time, a merging and compaction process in the background combines segment files, and discards overwritten or deleted values.
  • In most cases, a log is kept in addition to the current memtable, to restore the memtable during crashes. On a writeout, the current log is also discarded.

LSM-Trees

SSTables form the backbone of LSM-trees.

The algorithm can be slow when looking up a key that does not exist in the database, because it will have to look through the memtable and all segments before returning. Most database implementations such a LevelDB and RocksDB use a Bloom Filter, which is a memory-efficient data structure for approximating the contents of a set.

B-Trees

B-trees also keep key-value pairs sorted by key. Rather than the log-structured index, B-trees break the database down into fixed blocks or pages, traditionally 4kb in size, and read or write one page at a time.

Each page can be identified using an address or location, which allows one page to refer to another. These page references allow the construction of a tree of pages. B-trees are grown by splitting a page into 2 sub-pages.

A fundamental difference between B-trees and LSM-trees is that B-trees overwrite the page on disk with new data, whereas LSM-trees append to files and never modify files in place.

B-trees are made reliable via a write-ahead log: an append-only file to which every B-tree modification must be written to before it can be applied on the pages of the tree itself.

Comparing LSM-trees and B-trees

As a rule of thumb, LSM-trees are faster for writes (append-only), while B-trees are faster for reads. Reads are slower for LSM-trees because it has to look through multiple data structures to obtain the data. However, B-trees must write the data twice (to the write-ahead log, and to the tree itself).

Log-structured indexes also rewrite data multiple times, due to repeated compaction and merging of the SSTables. This is known as write amplification, and can wear out the hard disk.

LSM-trees have better compression, B-trees suffer from some fragmentation due to portions of pages being unused.

LSM-trees suffer from some performance degradation during the compaction process, interfering with ongoing reads and writes. Also, the bigger the database gets, the more disk bandwidth required for compaction.

Indexes

Both B-trees and LSM-trees can be used to build secondary indexes. The value of the index can be a reference to the row stored elsewhere, or the value of the row itself.

If using the reference method, the rows are typically stored in a heap file, in no particular order. The heap file approach avoids duplicating data when multiple secondary indexes are present. If the performance penalty of this extra lookup is too big, it can be desirable to store the row directly within the index.

Multi-dimensional Indexes

Spatial indexes such as R-trees are typically used here.

Full-text-search and Fuzzy Indexes

Full-text search engines commonly allow a search for one word to be expanded to include synonyms of the word, or ignore grammatical variations.

This requires a small in-memory index that is a sparse collection of some of the keys. In Lucene, this in-memory index is a Levenshtein automaton, which supports efficient search for words within a given edit distance.

Column-oriented Storage

Most OLTP databases are row-oriented: values for the full row are stored in the same location. Column-oriented storage store all the values from each column together. If each column is stored in a separate file, a query only needs to read and parse those columns that are used in that query.

In addition, column-oriented storage is good for compression.

How do columnar databases deal with slow writes? Vertica uses LSM-trees. All writes first go into an in-memory store, where they are added to a sorted tructure and prepared for writing to disk. When enough writes are accumulated, they are merged with the column files.

Many column-oriented databases store (replicate) their data in different sort orders. This is the equivalent to having multiple sort-indexes. Different sort orders cater to different types of queries.

Materialized Views

An aggregate function, such as COUNT, SUM, AVG, MIN or MAX in SQL, are commonly used in queries. Materialized views are actual copies of these results, written to disk, whereas virtual views are just shortcuts to writing the queries.

Data cubes are a special case of these materialized views. Aggregation is done by collapsing dimensions of the cube. These materialized views make certain queries very fast. However, data cubes are limited in their capability: they cannot perform queries for dimensions that are not part of the data cube.

Encoding and Evolution

One key goal is evolvability: systems that can adapt to change. This includes schema changes. Schema-on-read databases don’t enforce a schema, so the database can contain a mixture of older and newer data written at different times. For a system to continue running smoothly, we need to maintain compatibility in both directions:

Forward compatibility
newer code can read data that was written b y old code
Backward compatibility
older code can read data that was written by newer code

Backward compatibility is not hard to achieve, but forward compatibility requires older code to ignore additions made by a newer version of the code.

Programs often work with data in 2 representations:

  1. In-memory, where data is kept in data structures optimized for efficient access and manipulation by the CPU
  2. A self-contained sequence of bytes (e.g. JSON), used to write data to a file, or send over a network

Encoding and decoding is the task of translating between these representations.

Textual formats like JSON and XML are popular, but do not provide great compression ratio. Binary formats such as MessagePack provide much better compression ratio. Both Thrift and Protocol Buffers require a schema for any data that is encoded. An example of a Thrift interface definition looks like:

struct  Person {
  1: required string username,
  2: optional i64 number,
  3: optional list<string> interests
}

A code generation tool takes these definitions, and generate classes that implement this schema in various programming languages.

Schema Evolution

In Thrift and Protobuf, one can only add new fields to the schema, and not remove them. This ensures that a field’s tag always refers to the same field name.

Avro is another binary encoding format that’s different from Protocol Buffers or Thrift. Avro encodes data using a writer’s schema and decodes data using a reader’s schema. These schemas do not need to be the same, they only have to be compatible. Avro’s library resolves the differences between the two schema.

Avro is used in the context of Hadoop, for storing large files containing millions of records, all encoded with the same schema. The writer’s schema is stored at the beginning, along with all these records.

Dataflow

There are 3 main scenarios where data encodings are important:

  1. Databases, process of writing toand reading from the database
  2. RPC and REST APIs, where the client encodes a request, the server decodes the request and encodes a response, and finally the client decodes the response
  3. Asynchronous message passing (message brokers, actors) where nodes communicate by passing messages encoded and decoded by the sender or receipient