Google SWE teaches systems design | EP27: Search Indexes

Summary

This video script delves into search indexes, crucial for large-scale companies like Google and Amazon, offering fast query capabilities. It introduces Lucene, the popular open-source search index, and explains its architecture using LSM trees and SS tables for performance. The script covers tokenization, the creation of an inverse index, and advanced search functionalities like prefix and suffix searches. It then discusses Elasticsearch, which scales Lucene across a multi-node cluster, employing replication and partitioning for efficient searching and caching to boost performance. The video aims to clarify search index concepts, preventing common misconceptions.

Takeaways

• 🔎 Search indexes are crucial for large-scale companies with search functionalities, enabling quick and complex queries.
• 📚 Lucene is a widely-used search index technology, open-sourced since 1999 under the Apache license, and forms the basis for search platforms like Elasticsearch and Solr.
• 🌳 Lucene uses an LSM tree and SS table architecture for high performance, with writes first going to an in-memory buffer and then to disk every second during a refresh period.
• 📝 When a document is added to Lucene, it undergoes tokenization, which involves splitting terms based on rules, handling punctuation, case, and filtering out common words.
• 🔑 Lucene's architecture includes an inverse index, mapping terms to a list of document IDs, allowing for efficient searching by term.
• 🔍 Lucene supports advanced search features, including prefix searching, term suffix searching, and numeric term searching, with binary search used for finding documents matching a prefix.
• 🔄 Elasticsearch extends Lucene by scaling it across a multi-node cluster using replication and partitioning, creating local inverted indexes for each node.
• 💾 Elasticsearch achieves high read throughput by implementing caching effectively, including OS caching of index pages and caching of query results and components.
• 🗂️ Unlike global inverted indexes, Elasticsearch uses document partitioning, meaning each index on a node is relevant only for documents on that node.
• 🚀 The benefits of using search indexes like Elasticsearch include faster text searching capabilities compared to traditional database scans and the ability to scale in a distributed environment.
• 📈 The video script emphasizes the importance of understanding search index implementations to avoid misconceptions and to effectively utilize these technologies in applications.

Q & A

What is the primary purpose of search indexes in large-scale companies?

Search indexes are crucial for providing efficient search functionalities in applications. They optimize complex queries to run quickly, which is essential for companies like Google, Amazon, and Twitter that rely heavily on search capabilities.

Why are databases not ideal for search functionalities?

Databases are not optimized for search functionalities because they are not designed to handle the speed and complexity of search queries as efficiently as specialized search index technologies can.

What is Lucene and why is it significant in the context of search indexes?

Lucene is a high-performance, full-featured text search engine library created in 1999 and open-sourced under the Apache License. It serves as the foundation for many popular search platforms like Elasticsearch and Solr, making it a key component in the search index ecosystem.

Can you explain the LSM Tree and SS Table architecture used by Lucene?

The LSM Tree and SS Table architecture is a method used by Lucene to maintain high performance. It involves first writing data to an in-memory buffer, which is then periodically flushed to disk in the form of immutable SS Table index files. These files are later compacted and merged to maintain efficiency.

What happens during the tokenization process in Lucene?

Tokenization is the process of splitting text into individual elements or terms based on certain rules, typically whitespace. It also involves handling punctuation, case normalization, contractions, and filtering out common words that may not be relevant for search purposes.

What is an inverse index and how does it work?

An inverse index is the core of a search index, where instead of mapping a document ID to terms, each term is mapped to a list of document IDs that contain it. This allows for efficient searching as the index is sorted by terms, enabling quick retrieval of documents containing specific terms or term prefixes.

How does Lucene handle searches for term suffixes or numeric terms?

For term suffixes, Lucene can create a modified index where terms are stored in reverse order, allowing for the same prefix searching logic to be applied to suffixes. For numeric terms, Lucene may use different data structures or algorithms, such as comparing common digits in similar numbers.

What is Elasticsearch and how does it differ from Lucene?

Elasticsearch is a distributed, multitenant-capable full-text search engine built on top of Lucene. It scales Lucene across a multi-node cluster using replication and partitioning, creating local inverted indexes on each node relevant to the documents on that node.

How does Elasticsearch achieve high read throughput?

Elasticsearch achieves high read throughput through effective caching mechanisms. It caches index pages in memory, results of queries, and components of queries, allowing for quick retrieval and reuse of data for similar or repeated searches.

What are the advantages and disadvantages of document partitioning in Elasticsearch?

Advantages include efficient local search and write operations on a specific node. Disadvantages include the need to reach out to multiple shards for cross-shard searches, which requires merging results from different nodes.

Why are search indexes important for large applications?

Search indexes are important for large applications because they enable fast and efficient text searches, which would be impractical with traditional database scans and substring logic. They are essential for providing a seamless user experience in applications that require robust search capabilities.

Outlines

00:00

🔍 Introduction to Search Indexes

The video script begins with an introduction to search indexes, highlighting their importance in large-scale companies for efficient search functionalities. The speaker explains how traditional databases are not optimized for search operations and introduces Lucene as a popular search index technology, which has been open-sourced under the Apache license since 1999. Lucene forms the backbone for search platforms like Elasticsearch and Solr. The script then delves into Lucene's architecture, which utilizes an LSM tree and SS table structure to maintain high performance, with an emphasis on the write process involving an in-memory buffer and periodic refresh to disk. The summary also touches on the read process, which may involve multiple SS table files and merging results.

05:00

📚 Deep Dive into Lucene's Architecture and Features

This paragraph provides a deeper insight into Lucene's architecture, starting with the tokenization process of adding a document to Lucene, which involves splitting text into terms while handling punctuation, case sensitivity, contractions, and common words. The core of Lucene's functionality is its inverted index, which maps terms to document IDs, allowing for efficient searching. The script discusses how Lucene handles prefix searches using binary search on sorted term tables and introduces the concept of a modified index for suffix searches and numeric terms. It also mentions Lucene's capabilities for text similarity searches using Levenshtein distance and geospatial data, suggesting these topics for future videos. The paragraph concludes with an overview of Elasticsearch, which scales Lucene across a multi-node cluster using replication and partitioning, and highlights the benefits of Elasticsearch's caching mechanisms for achieving high read throughput.

Keywords

💡Search Indexes

Search indexes are a fundamental component in large-scale applications that require efficient search functionalities. They optimize the process of searching through large datasets by using specialized data structures and algorithms. In the video, search indexes are discussed as a critical part of companies like Google, Amazon, and Twitter, which rely on them for quick and complex queries. The script explains that databases alone are not sufficient for these functionalities, necessitating the use of search indexes to speed up search operations.

💡Lucene

Lucene is an open-source search index library that has been pivotal in the development of search functionalities since its inception in 1999. It is the underlying technology used by popular search platforms like Elasticsearch and Solr. The script describes Lucene as a highly performant search index that uses an LSM tree and SS table architecture, which is essential for handling write operations and maintaining quick search capabilities.

💡LSM Tree

The Log-Structured Merge-tree (LSM tree) is a data structure used by Lucene to enhance write performance. It is designed to handle large volumes of write operations efficiently by first storing them in an in-memory buffer and then periodically flushing this data to disk. The script mentions LSM trees as a key part of Lucene's architecture, which contributes to its high performance in write-heavy applications.

💡SS Table

SS Table, or Sorted String Table, is a file format used by Lucene for storing indexed data on disk. It is an immutable data structure that holds a sorted sequence of key-value pairs, which is essential for efficient searching. The script explains that writes are first buffered in memory and then written to an SS table, which is later involved in compaction and merging processes to maintain performance.

💡Tokenization

Tokenization is the process of breaking text into individual elements, or tokens, which are then used for indexing. It is a crucial step in preparing documents for search indexing, as it determines how search terms will be matched against the indexed content. The script discusses tokenization in the context of Lucene, where it involves handling punctuation, case sensitivity, contractions, and common words to ensure accurate and efficient searching.

💡Inverted Index

An inverted index is a data structure that stores a mapping from content, such as words or terms, to its locations in a database file, or a set of documents. It is the core of a search index, allowing for fast full-text searches. The script describes how Lucene uses inverted indexes to map terms to document IDs, enabling quick retrieval of documents containing specific terms or prefixes.

💡Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine built on top of Lucene. It scales Lucene's capabilities across a multi-node cluster, providing high availability and fault tolerance. The script explains that Elasticsearch uses replication and partitioning to distribute data across nodes, allowing for efficient searching and data management in large-scale environments.

💡Replication

Replication in the context of Elasticsearch refers to the process of duplicating data across different nodes in a cluster. It is a strategy used to enhance data availability and fault tolerance. The script mentions replication as a method by which Elasticsearch maintains copies of Lucene indices on multiple nodes, ensuring that the system can continue to operate even if some nodes fail.

💡Partitioning

Partitioning is the practice of dividing a dataset into smaller, more manageable pieces called partitions or shards. In Elasticsearch, partitioning allows an index to be distributed across multiple nodes, which can improve search performance and scalability. The script discusses how partitioning works in conjunction with replication to distribute the load and enhance the search capabilities of the system.

💡Caching

Caching is a technique used to store frequently accessed data in a faster storage medium, reducing the need to access slower storage systems. In Elasticsearch, caching is employed to improve the performance of search queries by storing the results and components of queries in memory. The script highlights the importance of caching in achieving high read throughput and fast query response times.

💡Levenstein Distance

Levenshtein distance is a string metric used to measure the difference between two sequences, which is useful in applications like fuzzy searching. It calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into another. The script briefly mentions Levenstein distance as a method that Lucene can use for searching similar words, which is part of its advanced search functionality.

Highlights

Search indexes are essential for large-scale companies with embedded search functionalities, optimizing complex queries for speed.

Lucene, an open-source search index since 1999, is widely used under the Apache license and powers search functionalities in platforms like Elasticsearch and Solr.

Lucene's architecture uses an LSM tree and SS table for high performance, with writes buffered in-memory and refreshed periodically to disk.

Lucene's tokenization process involves splitting documents into terms, handling punctuation, case, and common words to prepare for efficient searching.

The inverted index in Lucene maps terms to document IDs, allowing for rapid retrieval of documents containing specific terms.

Lucene supports prefix searches by utilizing sorted tables and binary search, enhancing the efficiency of term lookups.

For suffix searches, Lucene can use a modified index with reversed term characters to apply the same prefix search logic.

Lucene also handles numeric terms and geospatial data with specialized data structures, going beyond text-based searches.

Elasticsearch extends Lucene by scaling it across a multi-node cluster, using replication and partitioning to manage data distribution.

Elasticsearch creates local inverted indexes on each node, relevant only to the documents on that node, for efficient querying.

Elasticsearch benefits from caching strategies, including OS caching of index pages and caching of query results and components.

Caching in Elasticsearch contributes to high read throughput and fast performance, even for complex queries.

Search indexes enable large applications to find text strings much faster than traditional database scans, leveraging SS tables and inverted indexes.

Elasticsearch and Solr demonstrate the successful scaling of Lucene in a distributed environment, enhancing search capabilities.

The video creator emphasizes the importance of understanding search index implementations to avoid past mistakes and guide future development.

Transcripts

alrighty i am back i'm gonna be a little

bit behind schedule today just because

my roommate would not leave bed all day

and now i can finally record now that

he's gone so uh we're gonna talk about

search indexes which are a pretty

important part of any large-scale

company they pretty much all use them in

one sense or another so i'll talk about

the internals of those how they work and

then we can get through this video and

on to the next

alrighty search indexes what are they

well

a lot of companies have some sort of

search functionality embedded in their

application at one point or another i

mean if you're just taking google for

example it's pretty much their entire

business

amazon has the ability to search for pro

products based on a bunch of different

filters and you know you can do the same

on twitter for posts matching certain

keywords

but generally speaking databases aren't

great for these search functionalities

generally we want to use a different

type of data structure or

technology in general in order to

optimize for search functionality and

make it such that these very complex

queries can actually run really quickly

so what is leucine lucene is what's

known as a search index and it's

probably the most popular one it's been

around since 1999 and it's been open

sourced under the apache license

the actual search companies that people

tend to use which are you know

elasticsearch and solar

use leucine under the hood and as a

result of that i'm going to first

explain lucine and then i'll go into

elasticsearch and kind of how it builds

on that

so let's talk about the architecture of

lucian

generally speaking it uses an lsm tree

and ss table architecture and that's one

of the ways that it remains highly

performant so obviously first rights are

going to be sent to an in-memory buffer

the one difference here which is a

little bit nuanced for lucian is that

once the writes are sent to that buffer

you still can't read them just yet they

have to actually be propagated to the

disk first and in lucy and this tends to

happen once every second it's called

like a refresh period

so eventually the buffer is going to get

written to an immutable ss table index

file and then once there are a bunch of

ss table index files those are going to

get compacted and merged together which

is pretty typical for what we've seen

and then in terms of reads reads

actually have to be potentially sent to

multiple ss table files because each ss

table is kind of covering a different

set of documents and then once you get

the result of those reads you're going

to go ahead and merge them

okay so let's actually go ahead and

continue talking about this lucian

architecture because the ss table part

is only one aspect of it

so what actually happens when a document

is added to lucien well it has to be

tokenized which means that you're taking

all of the words or the terms in the

document and splitting them apart based

on some sort of rule typically you're

just splitting based on the white space

but then there are a few nuances as well

in terms of how we actually want to

handle the document because we don't

want to be mapping it to a bunch of

terms that don't really apply or that we

don't care about so obviously we have to

handle the punctuation

we have to handle you know the case of

words maybe it'd be better if we just

treated all words if they were lowercase

handling contractions such as like its

or their

you know with the apostrophe re and then

also handling common words like the or

and maybe we don't really care about

queries on these and we would rather

just filter them out

these are all also problems that occur

in natural language processing but just

tokenizing in general obviously you want

to be conscious of how you're actually

doing that because you want to be able

to search for these terms later and

you know if there are nuances within the

terms themselves you just have to think

about you know how am i actually going

to be tokenizing this document

okay so here's kind of the bread and

butter of a search index and this is

called an inverse index so inverse

indexes work by basically doing the

following once the document has been

tokenized we know that now it applies to

a list of terms so instead of mapping a

document id to a list of terms what we

actually do instead is take each term

and map it to a list of document ids

that contain it like i said this is

known as the inverted index and these

are what the ss table files are

generally containing

so that means that they're going to be

sorted by term by the way

but what if we want to just go beyond

looking at just one term itself and say

you know get all the terms with a prefix

of p e in this case

so let's start by finding all the

documents with those terms or at least

the terms that have that prefix and in

order to do so we can just run a binary

search on this sorted table and the way

you would probably do this is do two

binary searches where one gets the upper

bound so p-e-e in this case then one

gets the lower bound p-e-n

so you know that's how you would go

ahead and do that and that way we can

kind of find all the documents matching

this prefix in

login time

but generally speaking if we want to

just do searches that are more

complicated than just by term prefix we

have to add some additional logic and

here a couple of examples of that

what if we wanted to search by term

suffix so for example instead of

searching by p e we wanted to search for

all terms with e n

well what we could actually do here is

create a second modified index where

instead of storing the terms themselves

in the inverse index you store the

reversed characters of the term so that

way you can basically do the same prefix

searching logic but just with the suffix

and you go ahead and reverse that and

then run your binary search

another thing that's kind of similar is

doing this with numeric terms where you

actually look at kind of the the digits

of the number itself and that way you

can kind of say like oh you know like

let's look at the number 120 123

well they're kind of similar because

um there's actually or i don't know

maybe like

it's the fact that they basically have a

bunch of digits in common in the same

places and at the same start but i think

actually in reality lucian does use a

different data structure for things like

numbers and geospatial data so maybe

that's the topic for another video but

ultimately the whole point here is that

lucian can do a lot of really cool

search functionality and it's not just

on text but like i said numbers

you can do

text searching of similar words using

something called levenstein distance

which is you know done via some dynamic

programming algorithm and then also on

geolocation data which is a topic i will

cover in another video

okay so what is elasticsearch well

elasticsearch is basically taking

leucine and scaling it across a

multi-node cluster

so the way that we do this obviously is

going to be through some combination of

replication and partitioning

so we do have replication where each uh

thing that's being replicated is an

actual individual leucine index over a

bunch of documents

but also we have partitioning as well so

each index um you know can be sent to

certain nodes in the cluster and then

other index might be sent to different

nodes in the cluster and it's kind of in

this way that the data set is divided up

um so in that way elasticsearch

basically creates a bunch of local

inverted indexes keep in mind these are

not global inverted indexes it's not

like every single term or for a given

term we have every single document id of

it and then

that index is term partitioned but

rather it's document partitioned meaning

that

the index that's held on a given node is

only relevant for the documents on that

actual node so if you think about it

this has

you know obviously its advantages and

disadvantages it means that if you want

to search for terms across multiple

shards then you're actually going to

have to reach out to all of those shards

and merge together those results

but it also means that say we're you

know trying to implement a search

function on like a log functionality if

we put all of those log entries from the

same day on the same chart then we can

really quickly go ahead and make rights

to that shard and in addition to that

query it super easily

another huge benefit of elasticsearch

over just like plain old blue scene is

that it implements caching really well

and this is kind of how it achieves such

fast performance

so not only is there just like operating

system caching of index pages in memory

so if you think about it an ss table is

just a page on disk and um you know the

way that operating systems work is they

actually will tend to cache frequent

disk accesses so that way they can speed

up the actual you know accessing of the

index itself

but in addition to that elasticsearch

builds some functionality on top of that

where for a query on a given shard

basically it'll go ahead and cache the

result of that so you're not just

caching the index itself but say any

logic that you're doing with the index

or you know anything that you're adding

on top of that in the event that you're

going to end up doing that query again

and then furthermore

in addition to just caching the result

of the query um elasticsearch will

actually kind of cache components of

each query so that you know if a

separate query were to come along later

for a given shard and try and reuse some

of that data

then you know this could go ahead and be

really useful and you know if there are

parts of the queries that kind of

intersect

we already have that in our cache so

it's kind of in this sense that

elasticsearch is able to achieve really

high read throughput

okay ultimately search indexes are a

super important part of many large

applications

and it means that they can basically

find strings of text in a manner that's

much faster than just a traditional scan

over a database and kind of you know

using substring logic to try and find

the actual string itself

using ss tables in conjunction with an

inverted index turned out to be a really

good strategy for building a fast search

index and through

you know technologies such as

elasticsearch and solar

people have been able to go ahead and

scale out lucine in a manner such that

it works in a distributed environment

which is really great

also i'm really happy that i made this

video because i personally have looked

like a bozo in the past when bringing up

search indexes and then not knowing

about exactly how they were implemented

and then i felt like a real so

hopefully you guys don't make the same

mistake and i will be seeing you guys in

the next one