memgraph/docs/user_technical/examples.md

## Examples

This chapter shows you how to use Memgraph on real-world data and how to get interesting
and useful information out of it.

### TED Talks Example

[TED](https://www.ted.com/) is a nonprofit organization devoted to spreading ideas,
usually in the form of short, powerful talks.
Today, TED talks are influential videos from expert speakers on almost all
topics — from science to business to global issues.
Here we present a small dataset which consists of 97 talks. We'll show you how
to model this data as a graph and demonstrate a few example queries.

#### Data Model
Each TED talk has a main speaker, so we
identify two types of nodes — `Talk` and `Speaker`. Also, we will add
an edge of type `Gave` pointing to a `Talk` from its main `Speaker`. Each speaker has a name
so we can add property `name` to `Speaker` node. Likewise, we'll add properties
`name`, `title` and `description` to node `Talk`.
Furthermore, each talk is given in a specific TED event, so
we can create node `Event` with property `name` and relationship `InEvent`
between talk and event.

Talks are tagged with keywords to facilitate searching, hence we
add node `Tag` with property `name` and relationship `HasTag` between talk and
tag. Moreover, users give ratings to each talk
by selecting up to three predefined string values.
Therefore we add node `Rating` with these values as property `name` and relationship
`HasRating` with property `user_count` between talk and rating nodes.

#### Example Queries

We have prepared a database snapshot for this example, so you can easily import it
when starting Memgraph using the `--durability-directory` option.

```
/usr/lib/memgraph/memgraph --durability-directory /usr/share/memgraph/examples/TEDTalk \
  --durability-enabled=false --snapshot-on-exit=false
```

When using Memgraph installed from DEB package, you may need to stop the
currently running Memgraph server before you can import the example. Use the
following command:

```
systemctl stop memgraph
```

When using Docker, you can import the example with the following command:

```
docker run -p 7687:7687 \
  -v mg_lib:/var/lib/memgraph -v mg_log:/var/log/memgraph -v mg_etc:/etc/memgraph \
  memgraph --durability-directory /usr/share/memgraph/examples/TEDTalk \
  --durability-enabled=false --snapshot-on-exit=false
```

Now you're ready to try out some of the following queries.

NOTE: If you modify the dataset, the changes will stay only during this run of
Memgraph.

1) Find all talks given by specific speaker:
```
MATCH (n:Speaker {name: "Hans Rosling"})-[:Gave]->(m:Talk)
RETURN m.title;
```


2) Find the top 20 speakers with most talks given:

```
MATCH (n:Speaker)-[:Gave]->(m)
RETURN n.name, COUNT(m) as TalksGiven
ORDER BY TalksGiven DESC LIMIT 20;
```

3) Find talks related by tag to specific talk and count them:
```
MATCH (n:Talk {name: "Michael Green: Why we should build wooden skyscrapers"})-[:HasTag]->(t:Tag)<-[:HasTag]-(m:Talk)
WITH * ORDER BY m.name
RETURN t.name, COLLECT(m.name), COUNT(m) AS TalksCount
ORDER BY TalksCount DESC;
```

4) Find 20 most frequently used tags:
```
MATCH (t:Tag)<-[:HasTag]-(n:Talk)
RETURN t.name as Tag, COUNT(n) AS TalksCount
ORDER BY TalksCount DESC, Tag LIMIT 20;
```

5) Find 20 talks most rated as "Funny". If you want to query by other ratings,
possible values are: Obnoxious, Jaw-dropping, OK, Persuasive, Beautiful, Confusing,
Longwinded, Unconvincing, Fascinating, Ingenious, Courageous, Funny, Informative and
Inspiring.
```
MATCH (r:Rating{name:"Funny"})<-[e:HasRating]-(m:Talk)
RETURN m.name, e.user_count ORDER BY e.user_count DESC LIMIT 20;
```

6) Find inspiring talks and their speakers from the field of technology:
```
MATCH (n:Talk)-[:HasTag]->(m:Tag {name: "technology"})
MATCH (n)-[r:HasRating]->(p:Rating {name: "Inspiring"})
MATCH (n)<-[:Gave]-(s:Speaker)
WHERE r.user_count > 1000
RETURN n.title, s.name, r.user_count ORDER BY r.user_count DESC;
```

7) Now let's see one real-world example &mdash; how to make a real-time recommendation.
If you've just watched a talk from a certain speaker(e.g. Hans Rosling) you might be
interested in finding more talks from the same speaker on a similar topic:

```
MATCH (n:Speaker {name: "Hans Rosling"})-[:Gave]->(m:Talk)
MATCH (t:Talk {title: "New insights on poverty"})-[:HasTag]->(tag:Tag)<-[:HasTag]-(m)
WITH * ORDER BY tag.name
RETURN m.title as Title, COLLECT(tag.name), COUNT(tag) as TagCount
ORDER BY TagCount DESC, Title;
```

The following few queries are focused on extracting information about TED events.

8) Find how many talks were given per event:
```
MATCH (n:Event)<-[:InEvent]-(t:Talk)
RETURN n.name as Event, COUNT(t) AS TalksCount
ORDER BY TalksCount DESC, Event
LIMIT 20;
```

9) Find the most popular tags in the specific event:
```
MATCH (n:Event {name:"TED2006"})<-[:InEvent]-(t:Talk)-[:HasTag]->(tag:Tag)
RETURN tag.name as Tag, COUNT(t) AS TalksCount
ORDER BY TalksCount DESC, Tag
LIMIT 20;
```

10) Discover which speakers participated in more than 2 events:
```
MATCH (n:Speaker)-[:Gave]->(t:Talk)-[:InEvent]->(e:Event)
WITH n, COUNT(e) AS EventsCount WHERE EventsCount > 2
RETURN n.name as Speaker, EventsCount
ORDER BY EventsCount DESC, Speaker;
```

11) For each speaker search for other speakers that participated in same events:
```
MATCH (n:Speaker)-[:Gave]->()-[:InEvent]->(e:Event)<-[:InEvent]-()<-[:Gave]-(m:Speaker)
WHERE n.name != m.name
WITH DISTINCT n, m ORDER BY m.name
RETURN n.name AS Speaker, COLLECT(m.name) AS Others
ORDER BY Speaker;
```

Now you're ready to explore the world of graph databases with Memgraph
by yourself and try it on many more examples and datasets.

### Graph Gists Examples

A nice looking set of small graph examples can be found
[here](https://neo4j.com/graphgists/).  You can take any use-case and try to
execute the queries against Memgraph. To clear the database between trying out
examples, execute the query:

```
MATCH (n) DETACH DELETE n;
```