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"source": [
"# Hands-On Session for the tutorial: Multi-model Data query languages and processing paradigms in CIKM 2020\n"
]
},
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"metadata": {},
"source": [
"## 1: ArangoDB Installation"
]
},
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"source": [
"To better follow this hands-on session, please install the ArangoDB in advance. \n",
"\n",
"Please download and install the previous community builds (e.g., v3.4.0 https://download.arangodb.com/arangodb34/index.html) of ArangoDB \n",
"\n",
"Or you can install the lateset version by following the official instructions if your computer satisfies the requirement of v3.7.0:\n",
"\n",
"* https://www.arangodb.com/docs/stable/installation.html\n",
"\n",
"We recommend to use the ArangoDB WebUI to perform the queries, the default url is *localhost:8529*, and the default username is root with the empty password."
]
},
{
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"metadata": {},
"source": [
"## 2 Document store in ArangoDB : collections and documents\n",
"\n",
"*Relational databases* contain *tables* of *records* (as *rows*).\n",
"\n",
"An **ArangoDB document database** contains **collections** that contain **documents**. The documents follow the JSON format, and are usually stored in a binary format.\n",
"\n",
"Below is an example of json document containing information of a student and corresponding scores.\n",
"\n",
"** Score Document**\n",
"```\n",
"{\"_id\":0,\"name\":\"aimee Zank\",\n",
" \"scores\":[{\"score\":1.463179736705023,\"type\":\"exam\"},\n",
" {\"score\":11.78273309957772,\"type\":\"quiz\"},\n",
" {\"score\":35.8740349954354,\"type\":\"homework\"}]\n",
"}\n",
"```"
]
},
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"source": [
"### 2.1 warm-up: Loading the score documents\n",
"\n",
"### The score files is kept here. https://version.helsinki.fi/chzhang/cikm-2020-hands-on-session-for-multi-model-queries/-/blob/master/scores.json\n",
"\n",
"### You may create a collection named scores in the webUI and upload the file manully\n",
"\n",
"### or import the file to the server using arangoimp as follows:\n",
"arangoimp --file scores.json --collection scores --create-collection true"
]
},
{
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"metadata": {},
"source": [
"### 2.2 Arango Query Language (AQL) basics on documents"
]
},
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"source": [
"Basically, AQL includes the following operations:\n",
"\n",
" FOR: array iteration\n",
" \n",
" RETURN: results projection\n",
" \n",
" FILTER: results filtering\n",
" \n",
" SORT: result sorting\n",
" \n",
" LIMIT: result slicing\n",
" \n",
" LET: variable assignment\n",
" \n",
" COLLECT: result grouping\n",
" \n",
" INSERT: insertion of new documents\n",
" \n",
" UPDATE: (partial) update of existing documents\n",
" \n",
" REPLACE: replacement of existing documents\n",
" \n",
" REMOVE: removal of existing documents\n",
" \n",
" UPSERT: insertion or update of existing documents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 Arango Query Language (AQL) on documents"
]
},
{
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"execution_count": null,
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"source": [
"# Query 1: return a score document in the collection. \n",
"\n",
"For doc in scores Filter doc.name ==\"Leonida Lafond\" return doc"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"outputs": [],
"source": [
"# Query 2: (multiple conditions) return a score document in the collection. \n",
"For doc in scores Filter doc.name ==\"Leonida Lafond\" and doc._key=='266197464913' return doc"
]
},
{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": [
"# Query 3: (array operator 1) find types of scores.\n",
"For doc in scores limit 1 return doc.scores[*].type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"outputs": [],
"source": [
"# Query 4: (array operator 2) find students whose exam scores are greater than 90.\n",
"For doc in scores limit 1 return doc.scores[* Filter CURRENT.score>90].score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"# Query 5: (array operator 3) compute the average score.\n",
"For doc in scores limit 1 return AVERAGE(doc.scores[*].score)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"# Query 6: flatten\n",
"Return FLATTEN([ 1, 2, [ 3, 4 ], 5, [ 6, 7 ], [ 8, [ 9, 10 ] ] ])"
]
},
{
"cell_type": "code",
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"source": [
"# Query 7: sorting \n",
"For doc in scores\n",
" Sort first(doc.scores[*].score) DESC\n",
" Return doc"
]
},
{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": [
"# Query 8: grouping (with or without count)\n",
"For doc in scores\n",
" COLLECT name=doc.name into g\n",
" return {name,g}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"outputs": [],
"source": [
"# Query 9: define a variable using Let\n",
"FOR doc in scores \n",
" LET average_score=AVERAGE(doc.scores[*].score)\n",
" SORT average_score DESC \n",
" RETURN { name:doc.name,average_score:average_score}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"# Query 10: Inner join between two collections\n",
" FOR doc1 in collection1\n",
" FOR doc2 in collection2\n",
" Filter doc1.id==doc2.id\n",
" return {doc1:doc1,doc2:doc2}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Graph store : nodes and edges\n",
"\n",
"An ArangoDB graph database contains a set of node collections and edge collections."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Loading the example graphs"
]
},
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"source": [
"arangosh> var examples = require(\"@arangodb/graph-examples/example-graph.js\");\n",
"arangosh> var g = examples.loadGraph(\"knows_graph\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Traversing the graphs\n",
"\n",
"FOR vertex[, edge[, path]]\n",
" IN [min[..max]]\n",
" OUTBOUND|INBOUND|ANY startVertex\n",
" GRAPH graphName\n",
" [OPTIONS options]"
]
},
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"source": [
"# Query 11: find the friends of a given person. \n",
"\n",
"// get a random person p\n",
"Let p= (For person in persons Sort rand() limit 1 return person)\n",
"\n",
"// find the friends of p\n",
"FOR v,e,path\n",
"IN 1..1 any p[0]._id\n",
"GRAPH \"knows_graph\"\n",
"RETURN {p,v,e}"
]
},
{
"cell_type": "code",
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"source": [
"# Query 12: Filtering\n",
"# Filtering vertex \n",
"// get person bob\n",
"Let p= (For person in persons Filter person._key=='bob' return person)\n",
"\n",
"// find the friends of p\n",
"FOR v,e\n",
"IN 1..1 any p[0]._id\n",
"GRAPH \"knows_graph\"\n",
"Filter v._key=='alice'\n",
"RETURN {p,v,e}\n",
"\n",
"# Filtering path\n",
"// get person bob\n",
"Let p= (For person in persons Filter person._key=='bob' return person)\n",
"\n",
"// find the friends of p\n",
"FOR v,e,path\n",
"IN 1..2 any p[0]._id\n",
"GRAPH \"knows_graph\"\n",
"Filter length(path.edges)>1\n",
"RETURN {p,v,e,path}"
]
},
{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": [
"# Query 13: Graph functions -- Shortest Path\n",
"\n",
"// find the friends of p\n",
"FOR v,e\n",
"IN Any SHORTEST_PATH\n",
"'persons/charlie' to 'persons/alice'\n",
"GRAPH \"knows_graph\"\n",
"RETURN {v,e}"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"### 3. Your turn - Querying the UniBench datasets\n",
"\n",
"Download the UniBench dataset here and import them as follows:\n",
"\n",
"arangorestore dump --server.database handson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Questions\n",
"(1) How many unique types of vertices and unique labels of edges are there in two collections respectively? HINT: UNIQUE function\n",
"\n",
"(2) Some documents in collection imdb_vertices are associated with a \"releaseDate\" field. What is the newest movie in the collection? HINT: MAX function\n",
"\n",
"(3) Update a edge between \"imdb_vertices/crime\" and \"imdb_vertices/5541\" in collection imdb_edges with a label \"has_movie\", if the edge isn't exist, create one and insert it into the edge collection. HINT: keyword: UPSERT\n",
"\n",
"(4) For documents in collection imdb_vertices, find the ids that don't include any number, save them with a label into a new collection named \"genre\". (HINTs: use regex expression SUBSTRING(doc._id,14)=~ \"[a-zA-Z]\", create the genre collections beforehand)\n",
"\n",
"(5) Find actors whose name include \"David\", return documents that have the \"birthplace\" attribute. HINT: keyword like and HAS function\n",
"\n",
"(6) Find the actor who have acted in the most number of movies. HINT: keyword COLLECT\n",
"\n",
"(7) Regarding different movie genres, find the Top-5 genres with most number of movies in all time. HINT: keyword COLLECT\n",
"\n",
"(8) Return the number of persons who are both actor and director. HINT: SELF-JOIN ON imdb_edges\n",
"\n",
"(9) Given a movie \"Forrest Gump\", check its all associated actors. return their real names and role names. HINT: graph traversal\n",
"\n",
"(10) Given a actor \"Tom Hanks\", find the directors who have cooperated with him more than twice. HINT: graph traversal\n",
"HINT: graph traversal and COLLECT.\n",
"\n",
"(11) Think about a movie or actor you are interested in, visualize it in the ArangoDB and present some insights from the visulization."
]
}
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