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Elasticsearch
in action
By Thijs Feryn
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•My 8th DPC
•6th time as a speaker
•5th time as an “official” speaker
•DPC10 was my 1st international talk
•133 talks in 44 cities in 12 countries
By the numbers
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Thank you DPC
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Explain in 1 slide
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•Full-text search database
•NoSQL database
•Analytics engine
•Written in Java
•Lucene based ( ~Solr)
•Inverted indices
•Easy to scale (~Elastic)
•RESTFul interface (HTTP/JSON)
•Schemaless
•Real-time
•ELK stack
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Still with me?
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Hi, I’m Thijs
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I’m
@ThijsFeryn
on Twitter
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I’m an
at
Evangelist
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I’m an
at
Evangelist
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Hiring
Cloud Systems Engineers
in The Netherlands
https://www.unitt.com/nl/bedrijf/vacatures/cloud-systems-
engineer-devops-engineer
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I’m a
at
board member
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https://joind.in/talk/view/14221
I need
feedback
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https://www.elastic.co/
downloads/elasticsearch
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{
"status" : 200,
"name" : "War V",
"cluster_name" : "elasticsearch",
"version" : {
"number" : "1.5.2",
"build_hash" :
"62ff9868b4c8a0c45860bebb259e21980778ab1c",
"build_timestamp" : "2015-‐04-‐27T09:21:06Z",
"build_snapshot" : false,
"lucene_version" : "4.10.4"
},
"tagline" : "You Know, for Search"
}
http://localhost:9200
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RDBMS Elasticsearch
Database
Table
Row
Index
Type
Document
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POST /my-‐index
{"acknowledged":true}
POST/my-‐index/my-‐type
{
"key" : "value",
"date" : "2015-‐05-‐10",
"counter" : 1,
"tags" : ["tag1","tag2","tag3"]
}
{
"_index": "my-‐index",
"_type": "my-‐type",
"_id": "AU089olr9oI99a_rK9fi",
"_version": 1,
"created": true
}
CREATE DATABASE my-‐index
INSERT INTO my-‐index.my-‐type (key,date,counter,tags)
VALUES ("value","2015-‐05-‐10",1,"tag1,tag2,tag3")
Confirmation
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GET/my-‐index/my-‐type/AU089olr9oI99a_rK9fi?pretty
{
"_index": "my-‐index",
"_type": "my-‐type",
"_id": "AU089olr9oI99a_rK9fi",
"_version": 1,
"found": true,
"_source": {
"key": "value",
"date": "2015-‐05-‐10",
"counter": 1,
"tags": [
"tag1",
"tag2",
"tag3"
]
}
}
Retrieve
document by
id
Document &
meta data
SELECT *
FROM my-‐index.my-‐type
WHERE
id="AU089olr9oI99a_rK9fi"
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GET /my-‐index/_mapping?pretty
{
"my-‐index": {
"mappings": {
"my-‐type": {
"properties": {
"counter": {
"type": "long"
},
"date": {
"type": "date",
"format": "dateOptionalTime"
},
"key": {
"type": "string"
},
"tags": {
"type": "string"
}
}
}
}
}
}
Schemaless?
Not really …
“Guesses”
mapping on
insert
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Explicit mapping
… using actual data
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mysql> desc catalog_product_flat_1;
+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐+
| Field | Type | Null | Key | Default | Extra |
+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐+
| entity_id | int(10) unsigned | NO | PRI | NULL | |
| attribute_set_id | smallint(5) unsigned | NO | MUL | 0 | |
| type_id | varchar(32) | NO | MUL | simple | |
| allow_open_amount | int(11) | YES | | NULL | |
| cost | decimal(12,4) | YES | | NULL | |
| created_at | timestamp | YES | | NULL | |
| email_template | varchar(255) | YES | | NULL | |
| enable_googlecheckout | smallint(6) | YES | | NULL | |
| giftcard_amounts | decimal(12,4) | YES | | NULL | |
| giftcard_type | smallint(5) unsigned | YES | | NULL | |
| gift_message_available | smallint(6) | YES | | NULL | |
| gift_wrapping_available | smallint(6) | YES | | NULL | |
| gift_wrapping_price | decimal(12,4) | YES | | NULL | |
| has_options | smallint(6) | NO | | 0 | |
| image_label | varchar(255) | YES | | NULL | |
| is_recurring | smallint(6) | YES | | NULL | |
| is_redeemable | int(11) | YES | | NULL | |
| lifetime | int(11) | YES | | NULL | |
| links_exist | int(11) | YES | | NULL | |
| links_purchased_separately | int(11) | YES | | NULL | |
| links_title | varchar(255) | YES | | NULL | |
| msrp | decimal(12,4) | YES | | NULL | |
| msrp_display_actual_price_type | varchar(255) | YES | | NULL | |
| msrp_enabled | smallint(6) | YES | | NULL | |
| name | varchar(255) | YES | MUL | NULL | |
| news_from_date | datetime | YES | | NULL | |
| news_to_date | datetime | YES | | NULL | |
| open_amount_max | decimal(12,4) | YES | | NULL | |
| open_amount_min | decimal(12,4) | YES | | NULL | |
| price | decimal(12,4) | YES | MUL | NULL | |
| price_type | int(11) | YES | | NULL | |
| price_view | int(11) | YES | | NULL | |
| recurring_profile | text | YES | | NULL | |
| required_options | smallint(5) unsigned | NO | | 0 | |
| shipment_type | int(11) | YES | | NULL | |
| short_description | text | YES | | NULL | |
| sku | varchar(64) | YES | | NULL | |
| sku_type | int(11) | YES | | NULL | |
| small_image | varchar(255) | YES | | NULL | |
| small_image_label | varchar(255) | YES | | NULL | |
Magento’s
flattened product
table
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Import data
from MySQL to
ElasticSearch
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{
"category": [
"Default Category",
"Men",
"Shirts"
],
"entity_id": "231",
"created_at": "2013-‐03-‐05 06:48:12",
"updated_at": "2013-‐03-‐05 09:27:15",
"name": "French Cuff Cotton Twill Oxford",
"price": "190.0000",
"description": "Made with wrinkle resistant cotton twill, this French-‐cuffed
luxury dress shirt is perfect for Business Class frequent flyers.",
"sku": "msj000"
}
SELECT GROUP_CONCAT(distinct c.name) as category,p.entity_id, p.created_at,
p.updated_at, p.name, p.price, p.short_description as description,p.sku
FROM catalog_product_flat_1 p
JOIN `catalog_category_product_index` AS `cat_index` ON
cat_index.product_id=p.entity_id
JOIN `catalog_category_flat_store_1` AS `c` ON
cat_index.category_id=c.entity_id
WHERE cat_index.store_id = 1 AND cat_index.visibility=1
GROUP BY p.name
ORDER BY p.entity_id
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POST /products
{
"mappings": {
"product" : {
"_id" : {
"path" : "entity_id"
},
"properties" : {
"entity_id" : {"type" : "integer"},
"name" : {
"type" : "string",
"index" : "not_analyzed",
"fields" : {
"raw" : {
"type" : "string",
"analyzer": "english"
}
}
},
"description" : {
"type" : "string",
"index" : "not_analyzed",
"fields" : {
"raw" : {
"type" : "string",
"analyzer": "english"
}
}
},
"price" : {"type" : "double"},
"sku" : {"type" : "string", "index" : "not_analyzed"},
"created_at" : {"type" : "date", "format" : "YYYY-‐MM-‐dd HH:mm:ss"},
"updated_at" : {"type" : "date", "format" : "YYYY-‐MM-‐dd HH:mm:ss"} ,
"category" : {
"type" : "string",
"index" : "not_analyzed"
}
}
}
}
}
Explicit
mapping at
index creation
time
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"entity_id" : {"type" : "integer"},
"name" : {
"type" : "string",
"index" : "not_analyzed",
"fields" : {
"raw" : {
"type" : "string",
"analyzer": "english"
}
}
},
"description" : {
"type" : "string",
"index" : "not_analyzed",
"fields" : {
"raw" : {
"type" : "string",
"analyzer": "english"
}
}
},
"price" : {"type" : "double"},
"sku" : {"type" : "string", "index" : "not_analyzed"},
"created_at" : {"type" : "date", "format" : "YYYY-‐MM-‐dd HH:mm:ss"},
"updated_at" : {"type" : "date", "format" : "YYYY-‐MM-‐dd HH:mm:ss"} ,
"category" : {
"type" : "string",
"index" : "not_analyzed"
}
Not
analyzed?
English
analyzer?
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Analyzed
vs
non-analyzed
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Full-text
vs
exact value
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Analyzer
•Character filters
•Tokenizers
•Token filters
Replaces
characters
for analyzed
text
Break text
down into
terms
Add/modify/
delete tokens
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Built-in analyzers
•Standard
•Simple
•Whitespace
•Stop
•Keyword
•Pattern
•Language
•Snowball
•Custom
Standard
tokenizer
Lowercase
token filter
English
stop word
token filter
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Set the shape to semi-transparent by calling
set_trans(5)
set, the, shape, to, semi, transparent, by, calling,
set_trans, 5
set, the, shape, to, semi, transparent, by, calling,
set, trans
Set, the, shape, to, semi-transparent, by, calling,
set_trans(5)
set, shape, semi, transpar, call, set_tran, 5
Standard
Simple
Whitespace
English
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Building a custom analyzer
PUT /my_index
{
"settings": {
"analysis": {
"char_filter": {
"&_to_and": {
"type": "mapping",
"mappings": [ "&=> and "]
}},
"filter": {
"my_stopwords": {
"type": "stop",
"stopwords": [ "the", "a" ]
}},
"analyzer": {
"my_analyzer": {
"type": "custom",
"char_filter": [ "html_strip", "&_to_and" ],
"tokenizer": "standard",
"filter": [ "lowercase", "my_stopwords" ]
}}
}}}
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Search
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GET /products/product/_search?pretty
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 192,
"max_score": 1,
"hits": [
{
"_index": "products",
"_type": "product",
"_id": "231",
"_score": 1,
"_source": {
"category": [
"Default Category",
"Men",
"Shirts"
],
"entity_id": "231",
"created_at": "2013-‐03-‐05 06:48:12",
"updated_at": "2013-‐03-‐05 09:27:15",
"name": "French Cuff Cotton Twill Oxford",
"price": "190.0000",
"description": "Made with wrinkle resistant cotton
twill, this French-‐cuffed luxury dress shirt is perfect for Business
Class frequent flyers.",
"sku": "msj000"
}
},
…
SELECT * FROM
products.product
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GET /products/product/_search?pretty
POST /products/product/_search?pretty
{
"query": {
"match_all": {}
}
}
Search
“lite” vs full
query DSL
SELECT * FROM
products.product
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GET /products/product/_search?
pretty&q=name.raw:Blazer
POST /products/product/_search?pretty
{
"query": {
"match": {
"name.raw": "Blazer"
}
}
} SELECT * FROM
products.product
WHERE
name LIKE "%Blazer%"
Search
“lite” vs full
query DSL
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SELECT * FROM
products.product
WHERE
name = "Linen Blazer"
POST /products/product/_search?
pretty
{
"query": {
"match": {
"name.raw": "Linen Blazer"
}
}
}
POST /products/product/_search?
pretty
{
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"term": {
"name": "Linen Blazer"
}
}
}
}
}
Matches
2 products
Matches 1
product
SELECT * FROM
products.product
WHERE
name LIKE "%Linen%"
OR
name LIKE "%Blazer%"
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Filter
vs
Query
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Filter
•Does it match? Yes or no
•When relevance doesn’t matter
•Faster & cacheable
•For non-analyzed data
Query
•How well does it match?
•For full-text search
•On analyzed/tokenized data
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Match Query
Multi Match Query
Bool Query
Boosting Query
Common Terms Query
Constant Score Query
Dis Max Query
Filtered Query
Fuzzy Like This Query
Fuzzy Like This Field Query
Function Score Query
Fuzzy Query
GeoShape Query
Has Child Query
Has Parent Query
Ids Query
Indices Query
Match All Query
More Like This Query
Nested Query
Prefix Query
Query String Query
Simple Query String Query
Range Query
Regexp Query
Span First Query
Span Multi Term Query
Span Near Query
Span Not Query
Span Or Query
Span Term Query
Term Query
Terms Query
Top Children Query
Wildcard Query
Minimum Should Match
Multi Term Query Rewrite
Template Query
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And Filter
Bool Filter
Exists Filter
Geo Bounding Box Filter
Geo Distance Filter
Geo Distance Range Filter
Geo Polygon Filter
GeoShape Filter
Geohash Cell Filter
Has Child Filter
Has Parent Filter
Ids Filter
Indices Filter
Limit Filter
Match All Filter
Missing Filter
Nested Filter
Not Filter
Or Filter
Prefix Filter
Query Filter
Range Filter
Regexp Filter
Script Filter
Term Filter
Terms Filter
Type Filter
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Filter
examples
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POST /products/product/_search?pretty
{
"query": {
"filtered": {
"filter": {
"ids": {
"values": [231,234,258]
}
}
}
}
}
SELECT * FROM
products.product
WHERE
entity_id IN
(231,234,258)
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POST /products/product/_search?pretty
{
"query": {
"filtered": {
"filter": {
"bool": {
"must": [
{
"range": {
"price": {
"gte": 100,
"lte": 400
}
}
}
],
"must_not": [
{
"term": {
"name": "Convertible Dress"
}
}
],
"should": [
{
"term": {
"category": "Women"
}
},
{
"term": {
"category": "New Arrivals"
}
}
]
}
}
}
}
}
SELECT * FROM
products.product
WHERE
price BETWEEN 100 AND
400 AND
name != "Convertible
Dress" AND
(category = "Women"
OR category = "New
Arrivals")
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POST /products/product/_search?pretty
{
"query": {
"filtered": {
"filter": {
"prefix": {
"name": "Slim"
}
}
}
}
}
SELECT * FROM
products.product
WHERE
name LIKE "Slim%"
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{
"filtered" : {
"filter" : {
"geo_distance_range" : {
"from" : "200km",
"to" : "400km",
"pin.location" : {
"lat" : 40,
"lon" : -‐70
}
}
}
}
}
Requires “geo
point” typed
field
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Relevance
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{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 3.3350093,
"hits": [
{
"_index": "products",
"_type": "product",
"_id": "243",
"_score": 3.3350093,
"fields": {
"name": [
"Linen Blazer"
]
}
},
{
"_index": "products",
"_type": "product",
"_id": "246",
"_score": 0.5954286,
"fields": {
"name": [
"Stretch Cotton Blazer"
]
}
}
]
}
}
POST /products/product/_search?pretty
{
"fields": ["name"],
"query": {
"match": {
"name.raw": "Linen Blazer"
}
}
}
Hits both
terms. More
relevant
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{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "products",
"_type": "product",
"_id": "243",
"_score": 1,
"fields": {
"name": [
"Linen Blazer"
]
}
}
]
}
}
POST /products/product/_search?pretty
{
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"term": {
"name": "Linen Blazer"
}
}
}
}
}
No relevance
on filters
Score is
always 1
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POST /products/product/_search?pretty
{
"fields": ["name","description"],
"query": {
"bool": {
"must": [
{
"match": {
"description.raw": "suit"
}
}
],
"should": [
{
"match": {
"name.raw": "coat"
}
}
]
}
}
}
Only search
for “suit”
Increase
relevance if name
contains “coat”
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POST /products/product/_search?pretty
{
"fields": ["price","name","description","category"],
"query": {
"bool": {
"must": [
{
"filtered": {
"filter": {
"bool": {
"must": [
{
"range": {
"price": {
"gte": 100,
"lte": 400
}
}
},
{
"term": {
"category": "Men"
}
}
]
}
}
}
}
],
"should": [
{
"match": {
"category": "New Arrivals"
}
}
]
}
}
}
Increase
relevance
Combined
filters
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POST /products/product/_search?pretty
{
"query": {
"bool": {
"must": [
{
"match_all": {}
}
],
"should": [
{
"match": {
"description.raw": {
"query": "comfort",
"boost": 2
}
}
},
{
"match": {
"name.raw": {
"query": "tee",
"boost": 3
}
}
}
]
}
}
}
Increase
relevance
Query-
time
boosting
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Multi index
multi type
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SELECT *
FROM *.tbl_a*
WHERE
key LIKE ‘%val%’
•Cross-database queries
•Wildcard database
queries
•Wildcard table queries
Not
supported
in SQL
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/_search
/products/_search
/products/product/_search
/products,clients/_search
/pro*/_search
/pro*,cli*/_search
/products/product,invoice/_search
/products/pro*/_search
/_all/product/_search
/_all/product,invoice/_search
/_all/pro*/_search
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Multi
“all the
things”
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Aggregations
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Group by on steroids
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SELECT COUNT(category)
FROM products.product
GROUP BY category
Aggregations
in SQL
Metric
Bucket
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SELECT COUNT(category)
FROM products.product
GROUP BY category
POST /products/product/_search?
pretty&search_type=count
{
"aggs": {
"number_of_categories" : {
"cardinality": {
"field": "category"
}
}
}
}
Metric
Bucket
Only
aggs, no
docs
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POST /products/product/_search?pretty
{
"fields": ["category","price","name"],
"query": {
"match": {
"name.raw": "blazer"
}
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
},
"min_price" : {
"min": {
"field": "price"
}
},
"max_price" : {
"max": {
"field": "price"
}
},
"number_of_products_per_category" : {
"terms": {
"field": "category",
"size": 10
}
}
}
}
Multi-group
by
besides
query
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"aggregations": {
"min_price": {
"value": 455
},
"number_of_products_per_category": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Blazers",
"doc_count": 2
},
{
"key": "Default Category",
"doc_count": 2
},
{
"key": "Men",
"doc_count": 2
}
]
},
"max_price": {
"value": 490
},
"avg_price": {
"value": 472.5
}
}
Aggregation
output
Drill
down
search
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Min Aggregation
Max Aggregation
Sum Aggregation
Avg Aggregation
Stats Aggregation
Extended Stats Aggregation
Value Count Aggregation
Percentiles Aggregation
Percentile Ranks Aggregation
Cardinality Aggregation
Geo Bounds Aggregation
Top hits Aggregation
Scripted Metric Aggregation
Global Aggregation
Filter Aggregation
Filters Aggregation
Missing Aggregation
Nested Aggregation
Reverse nested Aggregation
Children Aggregation
Terms Aggregation
Significant Terms Aggregation
Range Aggregation
Date Range Aggregation
IPv4 Range Aggregation
Histogram Aggregation
Date Histogram Aggregation
Geo Distance Aggregation
GeoHash grid Aggregation
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Managing
Elasticsearch
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Plenty of ways
… for which we don’t have enough time
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Clustering
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Single
node
2 node
cluster
3 node
cluster
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Example config settings
node.rack: my-‐location
node.master: true
node.data: true
http.enabled: true
cluster.name: my-‐cluster
node.name: my-‐node
index.number_of_shards: 5
index.number_of_replicas: 1
discovery.zen.minimum_master_nodes: 2
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CAP theorem
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CAP theorem
Consistence
Availability
Partition tolerance
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GET /_cat
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GET /_cat
=^.^=
/_cat/allocation
/_cat/shards
/_cat/shards/{index}
/_cat/master
/_cat/nodes
/_cat/indices
/_cat/indices/{index}
/_cat/segments
/_cat/segments/{index}
/_cat/count
/_cat/count/{index}
/_cat/recovery
/_cat/recovery/{index}
/_cat/health
/_cat/pending_tasks
/_cat/aliases
/_cat/aliases/{alias}
/_cat/thread_pool
/_cat/plugins
/_cat/fielddata
/_cat/fielddata/{fields}
Non-JSON
output
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GET /_cat/shards?v
index shard prirep state docs store ip node
my-‐index 2 r STARTED 6 7.2kb 192.168.10.142 node3
my-‐index 2 p STARTED 6 9.5kb 192.168.10.142 node2
my-‐index 0 p STARTED 4 7.1kb 192.168.10.142 node3
my-‐index 0 r STARTED 4 4.8kb 192.168.10.142 node2
my-‐index 3 r STARTED 5 7.1kb 192.168.10.142 node1
my-‐index 3 p STARTED 5 7.2kb 192.168.10.142 node3
my-‐index 1 p STARTED 1 2.4kb 192.168.10.142 node1
my-‐index 1 r STARTED 1 2.4kb 192.168.10.142 node2
my-‐index 4 p STARTED 5 9.5kb 192.168.10.142 node1
my-‐index 4 r STARTED 5 9.4kb 192.168.10.142 node3
5 shards & a
single replica
by default
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GET /_cat/health?
v&h=cluster,status,node.total,shards,pri,unassign,init
cluster status node.total shards pri unassign init
mycluster green 3 12 6 0 0
Cluster health
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The ELK stack
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No content
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Logs
Parse
& ship
Store
Visualize
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Integrating
Elasticsearch
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It’s REST, deal
with it!
Directly
from
Javascript?
Don’t
forget to
“restrict”
access
AJAX #FTW
Behind a
proxy
No
backend
code
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Or just use a
backend API
PHP Java Perl
Python
Ruby
.NET
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Since this is a PHP
conference …
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Composer #FTW
{
"require": {
"elasticsearch/elasticsearch": "~1.0"
}
}
https://github.com/elastic/elasticsearch-php
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search($searchParams);
echo $results['hits']['hits'][0]["_source"]["rcpt"];
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No content
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https://joind.in/talk/view/14221
I need
feedback
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Hiring
Cloud Systems Engineers
in The Netherlands
https://www.unitt.com/nl/bedrijf/vacatures/cloud-systems-
engineer-devops-engineer