Data Science with Elasticsearch

DATA SCIENCE WITH
ELASTICSEARCH
FELIPE DORNELAS

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ABOUT ME
So ware Engineer
Data NERD
Electronic Music enthusiast
Work at ThoughtWorks

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WHAT IS
ELASTICSEARCH?

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A real-time distributed search and analytics engine

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Free and Open Source

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Distributed document store
Full-text search
Real-time analytics

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DISTRIBUTED DOCUMENT STORE
RESTful API
Automatic scale (Plug & Play)
Capable of handling petabytes of data

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FULL-TEXT SEARCH
Built on Lucene
Handles the human language:
Synonyms, typos and misspellings
Internationalization
Sort results by relevance score

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REAL-TIME ANALYTICS
Lots of aggregations and metrics
Gelocations
Can be combined with search
Real-time (no batch-processing)

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SEARCH

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STRUCTURED SEARCH (SQL)
"Does the document match the query?"
Yes or no question

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FULL-TEXT SEARCH
"How well does the document match the
search"?
Relevance score

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INVERTED INDEX

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ID Text
1 "
T
h
e q
u
i
c
k b
r
o
w
n f
o
x j
u
m
p
e
d o
v
e
r t
h
e
l
a
z
y d
o
g
.
"
2 "
Q
u
i
c
k b
r
o
w
n f
o
x
e
s l
e
a
p o
v
e
r l
a
z
y
d
o
g
s i
n s
u
m
m
e
r
.
"

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TOKENIZATION
ID Tokens
1 "
T
h
e
"
, "
q
u
i
c
k
"
, "
b
r
o
w
n
"
, "
f
o
x
"
,
"
j
u
m
p
e
d
"
, "
o
v
e
r
"
, "
t
h
e
"
, "
l
a
z
y
"
,
"
d
o
g
"
2 "
Q
u
i
c
k
"
, "
b
r
o
w
n
"
, "
f
o
x
e
s
"
, "
l
e
a
p
"
,
"
o
v
e
r
"
, "
l
a
z
y
"
, "
d
o
g
s
"
, "
i
n
"
,
"
s
u
m
m
e
r
"

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NORMALIZATION

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CAPITALIZATION
"
Q
u
i
c
k
" → "
q
u
i
c
k
"

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STEMMING
"
f
o
x
e
s
" → "
f
o
x
"

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REPLACING SYNONYMS
"
j
u
m
p
e
d
" ~ "
l
e
a
p
" → "
j
u
m
p
"

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REMOVING COMMON WORDS
"
t
h
e
"

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Term Doc #1 Doc #2
brown
dog
fox
in -
jump
lazy
over
quick
summer -

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SEARCH EXAMPLE
"
Q
u
i
c
k b
r
o
w
n f
o
x
e
s i
n s
u
m
m
e
r
?
"

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ELASTICSEARCH API
G
E
T /
e
x
a
m
p
l
e
/
d
o
c
u
m
e
n
t
/
_
s
e
a
r
c
h
{
"
m
a
t
c
h
"
: {
"
t
e
x
t
"
: "
Q
u
i
c
k b
r
o
w
n f
o
x
e
s i
n s
u
m
m
e
r
?
"
}
}

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QUERY IS ALSO NORMALIZED
"
q
u
i
c
k
"
, "
b
r
o
w
n
"
, "
f
o
x
"
, "
i
n
"
, "
s
u
m
m
e
r
"

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MATCHING THE INVERTED INDEX
Term Doc #1 Doc #2
quick
brown
fox
in -
summer -

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SEARCH RESULTS
"
h
i
t
s
"
: [
{
"
_
s
c
o
r
e
"
: 0
.
1
6
2
7
3
3
2
7
,
"
_
i
d
"
: "
2
"
,
"
_
s
o
u
r
c
e
"
: {
"
t
e
x
t
"
: "
Q
u
i
c
k b
r
o
w
n f
o
x
e
s l
e
a
p o
v
e
r l
a
z
y d
o
g
s i
n s
u
m
m
e
r
.
"
}
}
, {
"
_
s
c
o
r
e
"
: 0
.
0
1
2
7
3
3
2
7
,
"
_
i
d
"
: "
1
"
,
"
_
s
o
u
r
c
e
"
: {
"
t
e
x
t
"
: "
T
h
e q
u
i
c
k b
r
o
w
n f
o
x j
u
m
p
e
d o
v
e
r t
h
e l
a
z
y d
o
g
.
"
}
}
]

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SEARCH RESULTS
Document #
2 is a better match
Higher relevance score than #
1
Search results are sorted by relevance

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AGGREGATIONS

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BUCKETS + METRICS

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BUCKETS
Collection of documents that meet a certain criteria

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GENDER SOMEONE IDENTIFIES TO
Alice
⇒ female
Josh
⇒ male
Karen
⇒ non-binary

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CITIES FROM A STATE
San Francisco
⇒ California
Belo Horizonte
⇒ Minas Gerais

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DAYS FROM A MONTH
2
0
1
4
-
1
0
-
2
8
⇒ October
2
0
1
4
-
1
1
-
1
5
⇒ November

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METRICS
Calculations on top of Buckets
Ex: m
i
n
, m
a
x
, m
e
a
n
, s
u
m
…

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AGGREGATION EXAMPLE
partition citzens by state
then by gender
then by age ranges
then calculate average salary for each bucket (metric)

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Male
California
age < 21
Female Non-Binary
New York
21 < age < 50 age > 50
Texas
~ $ 5000/month
avg salary

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REAL-TIME ANALYTICS

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CAR TRANSACTIONS EXAMPLE
G
E
T /
c
a
r
s
/
t
r
a
n
s
a
c
t
i
o
n
s
/
A
V
F
r
1
x
b
V
m
d
U
Y
W
p
F
4
6
P
s
4
{
"
p
r
i
c
e
" : 1
0
0
0
0
,
"
c
o
l
o
r
" : "
r
e
d
"
,
"
m
a
k
e
" : "
h
o
n
d
a
"
,
"
s
o
l
d
" : "
2
0
1
4
-
1
0
-
2
8
"
}

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BEST SELLING CAR COLOR
G
E
T /
c
a
r
s
/
t
r
a
n
s
a
c
t
i
o
n
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
"
a
g
g
s
"
: {
"
c
o
l
o
r
s
"
: {
"
t
e
r
m
s
"
: {
"
f
i
e
l
d
s
"
: "
c
o
l
o
r
"
}
}
}
}

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BEST SELLING CAR COLOR
{
"
c
o
l
o
r
s
"
: {
"
b
u
c
k
e
t
s
"
: [
{
"
k
e
y
"
: "
r
e
d
"
, "
d
o
c
_
c
o
u
n
t
"
: 1
6
}
, {
"
k
e
y
"
: "
b
l
u
e
"
, "
d
o
c
_
c
o
u
n
t
"
: 8
}
, {
"
k
e
y
"
: "
g
r
e
e
n
"
, "
d
o
c
_
c
o
u
n
t
"
: 8
}
]
}
}

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AVERAGE CAR COLOR PRICE
G
E
T /
c
a
r
s
/
t
r
a
n
s
a
c
t
i
o
n
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
"
a
g
g
s
"
: {
"
c
o
l
o
r
s
"
: {
"
t
e
r
m
s
"
: { "
f
i
e
l
d
"
: "
c
o
l
o
r
" }
,
"
a
g
g
s
"
: {
"
a
v
g
_
p
r
i
c
e
"
: {
"
a
v
g
"
: { "
f
i
e
l
d
"
: "
p
r
i
c
e
" }
}
}
}
}
}

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AVERAGE CAR COLOR PRICE
{
"
c
o
l
o
r
s
" : {
"
b
u
c
k
e
t
s
"
: [
{
"
k
e
y
"
: "
r
e
d
"
, "
d
o
c
_
c
o
u
n
t
"
: 1
6
,
"
a
v
g
_
p
r
i
c
e
"
: { "
v
a
l
u
e
"
: 3
2
5
0
0
.
0 }
}
, {
"
k
e
y
"
: "
b
l
u
e
"
, "
d
o
c
_
c
o
u
n
t
"
: 8
,
"
a
v
g
_
p
r
i
c
e
"
: { "
v
a
l
u
e
"
: 2
0
0
0
0
.
0 }
}
, {
"
k
e
y
"
: "
g
r
e
e
n
"
, "
d
o
c
_
c
o
u
n
t
"
: 8
,
"
a
v
g
_
p
r
i
c
e
"
: { "
v
a
l
u
e
"
: 2
1
0
0
0
.
0 }
}
]
}
}

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CAR SALES REVENUE HISTOGRAM
G
E
T /
c
a
r
s
/
t
r
a
n
s
a
c
t
i
o
n
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
"
a
g
g
s
"
: {
"
p
r
i
c
e
"
: {
"
h
i
s
t
o
g
r
a
m
"
: {
"
f
i
e
l
d
"
: "
p
r
i
c
e
"
,
"
i
n
t
e
r
v
a
l
"
: 2
0
0
0
0
}
,
"
a
g
g
s
"
: {
"
r
e
v
e
n
u
e
"
: {
"
s
u
m
"
: { "
f
i
e
l
d
" : "
p
r
i
c
e
" }
}
}
}
}
}

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{
"
p
r
i
c
e
" : {
"
b
u
c
k
e
t
s
"
: [
{ "
k
e
y
"
: 0
, "
d
o
c
_
c
o
u
n
t
"
: 1
2
,
"
r
e
v
e
n
u
e
"
: { "
v
a
l
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e
"
: 1
4
8
0
0
0
.
0 }
}
,
{ "
k
e
y
"
: 2
0
0
0
0
, "
d
o
c
_
c
o
u
n
t
"
: 1
6
,
"
r
e
v
e
n
u
e
"
: { "
v
a
l
u
e
"
: 3
8
0
0
0
0
.
0 }
}
,
{ "
k
e
y
"
: 4
0
0
0
0
, "
d
o
c
_
c
o
u
n
t
"
: 0
,
"
r
e
v
e
n
u
e
"
: { "
v
a
l
u
e
"
: 0
.
0 }
}
,
{ "
k
e
y
"
: 6
0
0
0
0
, "
d
o
c
_
c
o
u
n
t
"
: 0
,
"
r
e
v
e
n
u
e
"
: { "
v
a
l
u
e
"
: 0
.
0 }
}
,
{ "
k
e
y
"
: 8
0
0
0
0
, "
d
o
c
_
c
o
u
n
t
"
: 4
,
"
r
e
v
e
n
u
e
"
: { "
v
a
l
u
e
" : 3
2
0
0
0
0
.
0 }
}
]
}
}

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TIME-SERIES DATA
Any data with a timestamp
Ex: server logs, sales history, stock prices

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HOW MANY CARS SOLD PER MONTH?
G
E
T /
c
a
r
s
/
t
r
a
n
s
a
c
t
i
o
n
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
"
a
g
g
s
"
: {
"
s
a
l
e
s
"
: {
"
d
a
t
e
_
h
i
s
t
o
g
r
a
m
"
: {
"
f
i
e
l
d
"
: "
s
o
l
d
"
,
"
i
n
t
e
r
v
a
l
"
: "
m
o
n
t
h
"
,
"
f
o
r
m
a
t
"
: "
y
y
y
y
-
M
M
-
d
d
"
}
}
}
}

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{
"
s
a
l
e
s
" : {
"
b
u
c
k
e
t
s
" : [
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
1
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 4 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
2
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 4 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
3
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 0 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
4
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 0 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
5
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 4 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
6
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 0 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
7
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 4 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
8
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 4 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
0
9
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 0 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
1
0
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 4 }
,
{ "
k
e
y
_
a
s
_
s
t
r
i
n
g
"
: "
2
0
1
4
-
1
1
-
0
1
"
, "
d
o
c
_
c
o
u
n
t
"
: 8 }
]
}
}

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COMMON SETUP - ELK
Logstash
⇒ Elasticsearch
⇒ Kibana

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LOGSTASH
Collect and stream logs into Elasticsearch

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KIBANA
An analytics dashboard for Elasticsearch

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MOVIE
RECOMMENDATIONS

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THE MOVIELENS DATA SETS
Movies Catalog
User Movie Recommendations (0 to 5)
User Movie Tags

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THE MOVIELENS 10M DATASET
10 million ratings
10,000 movies
72,000 users
Released on 2009

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M
O
V
I
E
S
.
D
A
T
M
o
v
i
e
I
D
:
:
T
i
t
l
e
:
:
G
e
n
r
e
s

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M
O
V
I
E
S
.
D
A
T
1
:
:
T
o
y S
t
o
r
y (
1
9
9
5
)
:
:
A
d
v
e
n
t
u
r
e
|
A
n
i
m
a
t
i
o
n
|
C
h
i
l
d
r
e
n
|
C
o
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(
.
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)

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R
A
T
I
N
G
S
.
D
A
T
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s
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r
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D
:
:
M
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R
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g
:
:
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s
t
a
m
p

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R
A
T
I
N
G
S
.
D
A
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2
:
:
1
1
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:
:
5
:
:
8
6
8
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6
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8
:
:
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:
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2
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:
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8
6
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2
4
4
6
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8
(
.
.
.
)

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LOADING DATA SETS
Python script to load the data sets into Elasticsearch
Using the new e
l
a
s
t
i
c
s
e
a
r
c
h
-
d
s
l library
One line at a time...

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LOADING DATA SETS
...it took too long to load the 10M data set
Improved time by using the bulk API

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THE MOVIE TYPE
G
E
T /
m
o
v
i
e
l
e
n
s
/
m
o
v
i
e
/
2
{
"
n
a
m
e
"
: "
J
u
m
a
n
j
i (
1
9
9
5
)
"
}

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THE USER RATING TYPE
G
E
T /
m
o
v
i
e
l
e
n
s
/
r
a
t
i
n
g
s
/
A
V
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1
x
b
V
m
d
U
Y
W
p
F
4
6
P
s
4
{
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s
e
r
"
: 1
5
,
"
r
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o
m
m
e
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d
_
m
o
v
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e
s
"
: [
1
2
2
, 1
8
5
, 2
3
1
, 2
9
2
, 3
1
6
, 3
2
9
]
}
Recommended movies have s
c
o
r
e >
= 4

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MOVIE RECOMMENDATIONS
Given Talladega Nights, starring Will Ferrel
We want to find comedies in similar style

View Slide

RECOMMENDING BASED ON POPULARITY
Find all users who recommended a movie
Agregate their recommendations
Take the top five most-popular

View Slide

Find the Talladega Nights ID:
G
E
T /
m
o
v
i
e
l
e
n
s
/
m
o
v
i
e
/
_
s
e
a
r
c
h
{
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q
u
e
r
y
"
: {
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m
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t
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h
"
: {
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t
i
t
l
e
"
: "
T
a
l
l
a
d
e
g
a N
i
g
h
t
s
"
}
}
}

View Slide

Find the Talladega Nights ID:
{
"
h
i
t
s
"
: [
{
"
_
i
d
"
: "
4
6
9
7
0
"
,
"
_
s
o
u
r
c
e
"
: {
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t
i
t
l
e
"
: "
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a
l
l
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d
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g
a N
i
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h
t
s
: T
h
e B
a
l
l
a
d o
f R
i
c
k
y B
o
b
b
y (
2
0
0
6
)
"
}
}
]
}

View Slide

Find the most popular movies from people who also like
Talladega Nights:
G
E
T /
m
o
v
i
e
l
e
n
s
/
r
a
t
i
n
g
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
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q
u
e
r
y
"
: {
"
f
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l
t
e
r
e
d
"
: {
"
f
i
l
t
e
r
"
: { "
t
e
r
m
"
: { "
m
o
v
i
e
"
: 4
6
9
7
0 } }
}
}
,
"
a
g
g
s
"
: {
"
m
o
s
t
_
p
o
p
u
l
a
r
"
: {
"
t
e
r
m
s
"
: { "
f
i
e
l
d
"
: "
m
o
v
i
e
"
, "
s
i
z
e
"
: 6 }
}
}
}

View Slide

A er correlating the ids to the titles, we got:
1. Matrix, The
2. Shawshank Redemption
3. Pulp Fiction
4. Fight Club
5. Star Wars Episode IV: A New Hope

View Slide

Very good list!
But almost everyone likes them!
These are universally well-liked movies.

View Slide

Findind the most popular movies from all the time:
G
E
T /
m
o
v
i
e
l
e
n
s
/
r
a
t
i
n
g
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
"
a
g
g
s
"
: {
"
m
o
s
t
_
p
o
p
u
l
a
r
"
: {
"
t
e
r
m
s
"
: { "
f
i
e
l
d
"
: "
m
o
v
i
e
"
, "
s
i
z
e
"
: 5 }
}
}
}

View Slide

1. Shawshank Redemption
2. Silence of the Lambs, The
3. Pulp Fiction
4. Forrest Gump
5. Star Wars Episode IV: A New Hope

View Slide

S
I
G
N
I
F
I
C
A
N
T
_
T
E
R
M
S
Aggregation based on statistics
Finds uncommonly common terms in a data set
i.e., statistic annomalies

View Slide

FOREGROUND GROUP
Popular movies among people who enjoy Taladega Nights

View Slide

BACKGROUND GROUP
Most popular movies among the entire user base

View Slide

Using the s
i
g
n
i
f
i
c
a
n
t
_
t
e
r
m
s aggregation:
G
E
T /
m
o
v
i
e
l
e
n
s
/
r
a
t
i
n
g
s
/
_
s
e
a
r
c
h
?
s
e
a
r
c
h
_
t
y
p
e
=
c
o
u
n
t
{
"
q
u
e
r
y
"
: {
"
f
i
l
t
e
r
e
d
"
: {
"
f
i
l
t
e
r
"
: { "
t
e
r
m
"
: { "
m
o
v
i
e
"
: 4
6
9
7
0 } }
}
}
,
"
a
g
g
s
"
: {
"
m
o
s
t
_
s
i
g
n
i
f
i
c
a
n
t
"
: {
"
s
i
g
n
i
f
i
c
a
n
t
_
t
e
r
m
s
"
: { "
f
i
e
l
d
"
: "
m
o
v
i
e
"
, "
s
i
z
e
"
: 6 }
}
}
}

View Slide

Returned movies:
{
"
a
g
g
r
e
g
a
t
i
o
n
s
"
: {
"
m
o
s
t
_
s
i
g
n
i
f
i
c
a
n
t
"
: {
"
d
o
c
_
c
o
u
n
t
"
: 2
7
1
,
"
b
u
c
k
e
t
s
"
: [
{ "
k
e
y
"
: 4
6
9
7
0
, "
d
o
c
_
c
o
u
n
t
"
: 2
7
1
, "
b
g
_
c
o
u
n
t
"
: 2
7
1 }
,
{ "
k
e
y
"
: 5
5
2
4
5
, "
d
o
c
_
c
o
u
n
t
"
: 5
9
, "
b
g
_
c
o
u
n
t
"
: 1
8
5 }
,
{ "
k
e
y
"
: 8
6
4
1
, "
d
o
c
_
c
o
u
n
t
"
: 1
0
7
, "
b
g
_
c
o
u
n
t
"
: 7
6
2 }
,
{ "
k
e
y
"
: 5
8
1
5
6
, "
d
o
c
_
c
o
u
n
t
"
: 1
7
, "
b
g
_
c
o
u
n
t
"
: 2
8 }
,
{ "
k
e
y
"
: 5
2
9
7
3
, "
d
o
c
_
c
o
u
n
t
"
: 9
5
, "
b
g
_
c
o
u
n
t
"
: 8
5
7 }
,
{ "
k
e
y
"
: 3
5
8
3
6
, "
d
o
c
_
c
o
u
n
t
"
: 1
2
8
, "
b
g
_
c
o
u
n
t
"
: 1
6
1
0 }
]
}
}
}

View Slide

1. Blades of Glory
2. Anchorman: The Legend of Ron Burgundy
3. Semi-Pro
4. Knocked Up
5. 40-Year-Old Virgin, The

View Slide

THANK YOU!
@felipead → , ,
GitHub Twitter MixCloud

View Slide

Data Science with Elasticsearch

Data Science with Elasticsearch

Felipe Dornelas

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