Making a simple Recommender System: Azure Cosmos Db and Apache Tinkerpop

Graph systems are one of the most ubiquitous models found in almost any natural and man-made structures we see everyday. Since computer science evolved around what we see and devise or deduce, graphs do play a huge role in day to day computation and programming techniques. It’s birth dates back to even before it was widely adopted in mathematics since mechanical computing was graph driven implicitly. If only we could get Alan Turing to talk over this.

Before I start, a big inspiration behind this write-up definitely goes to the work of Marko.A.Rodriguez . I was lucky to stumble upon his work while frolicking over the web and his works on graph computing systems and tinkerpop/gremlin is seriously inspiring.

What we are going to do today:

Let’s go right to what we want to do today. Our component of interest is the graph api of Azure Cosmos Db along with Apache Tinkerpop. Azure Cosmos Db has a graph api that allows us to store data as a graph or a network. In simple words, instead of storing data in a tabular format where you have row and column, you can store data as they describe each other in terms of relations. If the text here is not really doing it for you, let’s jump into some examples.

Let’s have a look into this sample graph I made from HIMYM. The big ellipses and boxes here are called vertices and the lines that connect them are called edges. Since we have a little arrow head telling the directions of them, this is actually a Directed Graph. If we look at the data behind the graph, then you can see, we can represent this in two ways. In a table, you can list up the vertices in one table and the edges in other. The edges table might look like:

From To Type
Ted Barney  friend
Ted Umbrella  found
Barney Robin  wife
Robin Barney  husband
Tracy Umbrella  lost
Ted Tracy  lost
….

I didn’t add all the rows of course and if you look at the design you can see it is not properly normalized. That means in understandable terms that we needed a Type table with all the edge types so we don’t write a Type twice. But that is not in this article’s scope. So, let’s ignore that. The thing here to see is, graph nodes/vertices can be of different types and the edges can mean different relationships. Like, here there are vertices that doesn’t represent a character from HIMYM, like the Umbrella and the MacLaren’s Pub. Storing these data in a regular database is possible and has been done many times. This way is called the ‘implicit’ way to store graph like data. Now one might think, by this rule all data in the world can be represented by a graph. And it is indeed true. But we use a graph database only when we see that the data itself focuses more on relations than the data content itself. Like friend graph in a social network website. Or a geographical model of all the offices of a big corporation. The vertices do carry data but the relations are really important here. In these case, a graph database comes in very handy. Azure Cosmos Db recently came up with a graph api and it can store graph data natively where the cost to traverse a graph is constant. In a regular database we have to join multiple tables to formulate these relations properly and in a lot of cases, these computational costs are not constant.

Apache Tinkerpop joins the party:

I hope by now we understand why we need a graph database and now is the right time we talk about Apache Tinkerpop. It was previously known as Apache Gremlin. This is essentially a fantastic graph computing engine which allows you to connect to multiple graph database using a single domain specific language construct. It has language supports for most of the popular languages and it is pretty native to groovy and java. Fret not, we have our way to use it over here with C# too. Before we start we need to create a simple graph database on Azure. The quickstart is here. You can also opt for java and nodejs. I personally used an Azure CosmosDb emulator. The quickstart to install that instead of an actual Azure CosmosDb is here. You can opt for any of these but I have to remind you, at the time this article is being written, Azure CosmosDb Emulator do not support creating graphs and browsing graph data through it’s local web portal. You have to use Gremlin Console to connect and talk to the local emulator. Gremlin Console is a command line REPL that you can use to traverse a local gremlin/tinkerpop graph or a remote one. It can connect to any gremlin servers anywhere as long as you have the credentials. You can use the gremlin console to talk to the actual Azure CosmosDb graph database. I suggest using Windows Subsystem For Linux (WSL), better known as “Bash on Ubuntu on Windows” for using gremlin-console. The gremlin console quickstart is here.

Our sample data set today:

We are going to use the movie review data set from grouplens from here . We are using the small dataset of about 100,000 ratings applied to 9,000 movies by 700 users. If you download and unzip the data you will see multiple csv fields. Of which I only used the movies.csv and ratings.csv files. I made a separate users.csv file for the users list. Don’t worry, all these are attached with the sample code. The movies csv comes with the movie id, movie name  and genres column. The ratings.csv comes with the user id, movie id and a rating column where the user has rated the movie from 0 to 5. The sample code has a simple command line uploader tool that will let you upload the data in your desired gremlin server and graph database connected to it. So, to understand how we talk to gremlin, let’s have a peek at the code, shall we?

Let’s talk code:

I’m not going to focus on the full source at a time.  Let’s have a look on how can we connect to an existing Azure CosmosDb Graph database. First, we create a DocumentClient. 


DocumentClient client = new DocumentClient(
 new Uri(endpoint),
 authKey,
 new ConnectionPolicy { ConnectionMode = ConnectionMode.Direct, ConnectionProtocol = Protocol.Tcp });

Definitely the thing that you will notice missing is the authkey variable here. And that is actually the primary key of your graph database that you will find in your azure portal. If you use the emulator they use a fixed key which you will find in the quickstart link I shared above.

Now that we do have a DocumentClient let’s upload the sets of movies that we will use for reviews. I’m using the movie name and the movie id for the sake of simplicity here. In our graph, the movie vertices/nodes will have a label ‘movie’. This sets the type of the vertex and we will set the name and id as properties of this vertex. The id uniquely distinguish any vertex. So remember, no matter what the label of the vertex is the id has to be unique. Or Azure CosmosDb will let you know that it can not add a vertex because already a vertex of that same key exists. If you don’t provide an id property, Azure CosmosDb will generate one and put it on the vertex.

We make sure at the beginning of the upload to drop the existing graph database if there is any.

        private async Task NukeCollection(DocumentClient client)
        {
            try
            {
                Console.WriteLine("Nuking...");
                var response = await client.DeleteDocumentCollectionAsync(UriFactory.CreateDocumentCollectionUri("graphdb", "Movies"));
                Console.WriteLine(response.StatusCode);
            }
            catch (DocumentClientException ex)
            {
                Console.WriteLine(ex.Message);
            }
        }

The database name I used for these sample is graphdb and the collection name is Movies. Pardon me for my indecency to hard code these but it’s a sample code, so I put effectiveness in front of decency.

Adding vertices to our ‘Movies’ graph:

Now, we upload the movies. Of course make sure we create the database if it’s already not there.

        private async Task UploadMovies(DocumentClient client)
        {
            try
            {
                Console.WriteLine("Uploading movies");
                Database database = await client.CreateDatabaseIfNotExistsAsync(new Database { Id = "graphdb" });

                DocumentCollection graph = await client.CreateDocumentCollectionIfNotExistsAsync(
                    UriFactory.CreateDatabaseUri("graphdb"),
                    new DocumentCollection { Id = "Movies" },
                    new RequestOptions { OfferThroughput = 1000 });

                Console.WriteLine("Connected to graph Movies collection");

                Console.WriteLine("Reading movie list");
                using (TextReader reader = new StreamReader("movies2.csv"))
                using (CsvReader csv = new CsvReader(reader))
                {
                    while (csv.Read())
                    {
                        string idField = csv.GetField<string>(0);
                        string titleField = csv.GetField<string>(1);
                        titleField = JsonConvert.ToString(titleField, '\"', StringEscapeHandling.EscapeHtml);

                        Console.WriteLine("Uploading " + titleField);

                        IDocumentQuery<dynamic> query = client.CreateGremlinQuery<dynamic>(graph, $"g.addV('movie').property('id', '{idField}').property('title', {titleField})");
                        while(query.HasMoreResults)
                        {
                            await query.ExecuteNextAsync();
                        }
                    }
                }
            }
            catch (DocumentClientException ex)
            {
                Console.WriteLine(ex.Message);
            }
        }

For the collection, same strategy is followed. We get a DocumentCollection instance trying to create the Movies collection if it doesn’t exist or just fetching the existing one if there is one already. Then we start reading the csv file. We use the same DocumentClient instance to create a gremlin construct to add a vertex/node in the collection for each of the movies. We add a Movie label along with the title and id  property as promised. I want to focus a little bit in the gremlin/tinkerpop construct we used here.

The full construct in a more readable format is:

g.addV('movie')
    .property('id', '{idField}')
    .property('title', {titleField})

Let’s ignore the idField and titleField as we already know what is there. The first construct g  stands for the graph in the collection. addV(‘label’) is the method construct to add a vertex. You can see the whole construct is design wise fluent. The next two property(propetyName, propertyValue) construct adds two properties in the newly added movie node. Nice builder interface, isn’t it? Pretty expressive. We are using the groovy standard constructs for gremlin. There are *other language constructs too, including javascript. And following the same approach, I uploaded the users in the sample code too.

By the time of writing this article, the whole connector library from nuget is in preview version and I don’t have one for .net core. So, we need to sit this one out for .net core. Sorry for that.

Connecting the vertices with edges:

The next thing in line is of course to add edges that represents the relationship between an user and a movie. We label the relationships with ‘rates’ and also will add a property named weight and it’s value will be the actual rating value given by that user.

        private async Task UploadReviews(DocumentClient client)
        {
            try
            {
                Console.WriteLine("Uploading movie reviews");

                DocumentCollection graph = await client.CreateDocumentCollectionIfNotExistsAsync(
                    UriFactory.CreateDatabaseUri("graphdb"),
                    new DocumentCollection { Id = "Movies" },
                    new RequestOptions { OfferThroughput = 1000 });

                Console.WriteLine("Connected to graph Movies collection");

                Console.WriteLine("Reading review list");
                using (TextReader reader = new StreamReader("ratings2.csv"))
                using (CsvReader csv = new CsvReader(reader))
                {
                    while (csv.Read())
                    {
                        string userId = "user" + csv.GetField<string>(0);
                        string movieId = csv.GetField<string>(1);
                        float rating = csv.GetField<float>(2);

                        Console.WriteLine("Uploading review for user " + userId + " to " + movieId + " with rating "+ rating);
                        IDocumentQuery<dynamic> query = client.CreateGremlinQuery<dynamic>(graph, $"g.V().hasLabel('user').has('id', '{userId}').addE('rates').property('weight', {rating}).to(g.V().has('id', '{movieId}'))");
                        while (query.HasMoreResults)
                        {
                            var result = await query.ExecuteNextAsync();
                            foreach (var item in result)
                            {
                                Console.WriteLine(item);
                            }
                        }
                    }
                }
            }
            catch (DocumentClientException ex)
            {
                Console.WriteLine(ex.Message);
            }
        }

The only noticeable change here in this snippet that we created edges between a movie and an user vertex. Like before, let’s zoom in the gremlin construct we used this time to create an edge between two nodes.

g.V()
    .hasLabel('user')
    .has('id', '{userId}')
    .addE('rates')
    .property('weight', {rating})
    .to(g.V()
    .has('id', '{movieId}'))

The first enumeration g.V() should enumerate all the vertices in the graph. We need to filter the user vertex first to create an edge from. The next hasLabel(”user) filters all the user vertices. Subsequently .has(‘id’, ‘{userId}’) filters the vertex of the user we want bearing that user id. Then we use addE(‘rates’) method to ensure that we add an edge labeled ‘rates’ from it. The following property(‘weight’, {rating}) should add the weight property with the rating as value on the edge we just created. The last thing we do is to tell where this edge points to. With to(g.V() .has(‘id’, ‘{movieId}’))  we filter out the movie node we want and use it to define which vertex the edge points to. Decent huh? You can find the full tinkerpop reference here.

By now a single user rating a movie should look something like:

Finally, we have all our data ready. Time to traverse this graph and make a simple movie recommendation for any user. 🙂

The simplest movie recommender system in this world:

Let’s devise a simple movie recommender and we will follow the dumbest real life approach we see around. Usually when I pick a movie to see, I pick a movie based on the movies I have already seen and liked. If I liked Deadpool, there’s a very good chance I will like Deadpool 2 and other superhero movies like Logan. Remember we didn’t add/use any genre data in our graph. All we have here are the users, the movies and the ratings made by the users on these movies.

So, let’s find out the other users who likes the same movies as our reference user do. Our reference user is the user who has asked for a recommendation for movies that he should see from us. He has only given us a list of movies he saw and rated. Our current traversed graph should look like this:

We want to traverse the users who like the same movies our reference user likes using gremlin. To construct the query, first we need to get out of our user vertex and find out movies that our user likes. The gremlin construct for that will be:

g.V()
    .hasLabel('user')
    .has('id', 'user7')
    .outE('rates')
    .has('weight', gte(4.5))

Let’s assume our reference user id is user7. And at first we filter the vertices with label user and id user7. After that we use outE(‘rates’) to traverse edges that is labeled rates and has weight more than or equal to 4.5. That’s how we will land on the nodes that we think the user likes. But right now, we are standing on the edges. To land on the movie nodes, we have to use:

g.V()
    .hasLabel('user')
    .has('id', 'user7')
    .outE('rates')
    .has('weight', gte(4.5))
    .inV()
    .as('exclude')

inV() construct will enumerate all the attached movie nodes with the rates edges. The as(‘exclude’) construct is used for a specific purpose. For now, all I can say is, I’m marking all the movies I saw as ‘exclude’.  We will see why I’m doing this soon.

So, now we want to find out all the other users who likes the movies our reference user likes.

So, this is where we want to end up. We found user2 and user3 likes the same movies almost as much our reference user likes. To progress through gremlin, our new gremlin query is:

g.V()
    .hasLabel('user')
    .has('id', 'user7')
    .outE('rates')
    .has('weight', gte(4.5))
    .inV()
    .as('exclude')
    .inE('rates')
    .has('weight', gte(4.5))
    .outV()

We added a InE(‘rates’) construct since we want to know now which inward edges with label ‘rates’ are pointing to these movies our reference user likes. We also filter the edges more with weight property value greater than or equal to 4.5 since we only want users who likes the same movies. At last we added outV() to find the users attached to these edges. Now we are standing on the users who likes the same movies our reference user does.

We want to know what other movies these users likes that our reference user has not rated or seen yet. I’m assuming our reference user rated all the movies he has seen.

From the graph above we can clearly see that user3 and user2 likes movie4 and movie5 that our current user has not rated yet. These are viable candidates for our users movie recommendation. It definitely seems naive, but it’s a start. If you remember about the exclude nodes, we are going to use these now to make sure that our recommender doesn’t recommend the movies our user already has seen. Our desired gremlin query is:

g.V()
    .hasLabel('user')
    .has('id', 'user7')
    .outE('rates')
    .has('weight', gte(4.5))
    .inV()
    .as('exclude')
    .inE('rates')
    .has('weight', gte(4.5))
    .outV()
    .outE('rates')
    .has('weight', gte(4.5))
    .inV()
    .where(neq('exclude'))

We traveled the movies all these other user likes and made sure that we exclude the ones we have already seen using  where(neq(‘exclude’)). To fix our naivety a little bit, let’s take the distinct movies using dedup() and order it by the movies by the amount of ratings it has received.

g.V()
    .hasLabel('user')
    .has('id', 'user7')
    .outE('rates')
    .has('weight', gte(4.5))
    .inV()
    .as('exclude')
    .inE('rates')
    .has('weight', gte(4.5))
    .outV()
    .outE('rates')
    .has('weight', gte(4))
    .inV()
    .where(neq('exclude'))
    .dedup()
    .order().by(inE('rates').count(), decr)
    .limit(10)
    .values('title')

This has to be really really naive to survive any production requirement. But it is indeed an eye opener on how Apache Tinkerpop and Azure CosmosDB Graph databases can do.

If you look on the final query quickly you will see we ordered the final movie nodes by the count of incoming rates edges it has and ordered it in a descending order using order().by(inE(‘rates’).count(), decr). We limited the nodes to first 10 and we only took the titles of the movies.

Putting the system to test:

I wrote a simple REPL to try out various gremlin commands towards our Azure CosmosDb.  Our reference user id was ‘user7’. The list of the movies seen by our reference user is: MoviesSeenByUser

If it is too hard to read, let’s put out the movies here:

  • “Braveheart (1995)”
  • “Star Wars: Episode IV – A New Hope (1977)”
  • “Shawshank Redemption, The (1994)”
  • “Wallace & Gromit: The Best of Aardman Animation (1996)”
  • “Wallace & Gromit: A Close Shave (1995)”
  • “Wallace & Gromit: The Wrong Trousers (1993)”
  • “Star Wars: Episode V – The Empire Strikes Back (1980)”
  • “Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)”
  • “Star Wars: Episode VI – Return of the Jedi (1983)”
  • “Grand Day Out with Wallace and Gromit, A (1989)”
  • “Amadeus (1984)”
  • “Glory (1989)”
  • “Beavis and Butt-Head Do America (1996)”

The movies our simple recommender suggested are:

  • “Forrest Gump (1994)”,
  • “Pulp Fiction (1994)”,
  • “Fargo (1996)”,
  • “Silence of the Lambs, The (1991)”,
  • “Star Trek: Generations (1994) “,
  • “Jurassic Park (1993)”,
  • “Matrix, The (1999)”,
  • “Toy Story (1995)”,
  • “Schindler’s List (1993)”,
  • “Terminator 2: Judgment Day (1991)”

Clearly this is not the best recommender engine, but definitely one of the simplest. We can always reuse the genome data along with the dataset and the genre data and do a proper collaborative filtering. But the scope of this article was just to demonstrate the capabilities of a simple graph traversal.

Hope it was fun to read. Try out Azure Cosmos Db Graph Api and Apache Tinkerpop if you can. They are really fun together to use to and there’s so much you can do using simple graph traversals.

The sample code is hosted here in github. Until next time!

 

Building your own REST query language: An experiment with Azure Cosmos Db

For all the REST resources we deploy out every day, one of the most common scenario a developer ends up handling is designing a nice and effective search/filter/query endpoint for these aforementioned resources. Usually in these scenarios conventions play a big part and that starts from query filter parameters in the query string to using a full blown search engine like ElasticSearch, Lucene or Algolia. Vinay Sahni did a phenomenal job for putting out the gist for basic REST practices here. If you are new into REST and want to get yourself started talking REST, it’s worth a look.

Today, we will do a simple yet fun experiment. Our goal today is to create a simple nifty looking query language for a sample REST resource. We will try to mimic a couple of features Twitter Search Api provides and our sample data storage today will be Azure Cosmos Db: Document Db which previously was known as just Azure Document db. Recently it moved to the Azure Cosmos Db family and if you have missed it, here is the full details.

Therefore, the first thing we need is a set of sample data we can use as our REST resource. Since we are going to mimic the twitter public search api, we definitely need some sample data first. And of course github comes to the rescue. Download the sample data and put it to your own document db instance. If you don’t know how to create a document db instance, follow the quick start here. I’m referencing the link to the .net quick start, but there are node.js, Python, Java and Xamarin resources right by the side of it. For those of you who do not want to test your work against an Azure hosted document db and want to test Azure document db locally, you can always opt for the azure document db emulator.

Now, for the sake of this experiment I ported the github twitter sample data to an azure document db instance. It has 10 entries in that small data set and hosted here. If you want to browse the data, azure portal does allow it. But I suggest looking at this open source app here named Azure DocumentDB Studio. You can use the endpoint and the key written in the sample code in github to connect. The database is public just for you guys so you can test the sample code I have hosted in github and will list out in the end of this article. TL;DR people, this is your cue to go to the end of the article, but I highly suggest to stick around.

Features to be built:

Out of all the features twitters search api provides, I will port the to:UserAccount, from:UserAccount and “exact text” search capabilities. That means, We will be able to search tweets sent from one account to other and we will also be able to search tweets by mentioning a string we like to be present in the tweets. For an example if we want to find tweets

  1. from an user account named @terminator
  2. to another user account named @robocop 
  3. or any tweet where the words “I’m back”
  4. or the hashtag #HastaLaVista is present,

our sample query language excerpt will be

http://our-awesome-api/search/q?to:robocop AND from:terminator OR “I’m back” OR #HastaLaVista”

If you have an eye for detail, you will notice that this is not exactly like the twitter search api since doesn’t use an and operator. But for the sake of the simplicity in this example this should be enough.

Tools we are going to use:

Our api stack will be written in asp.net core. We will use ANTLR as our query language lexer-parser. If you need an ANTLR primer and another experiment I did with it, please have a look here where I tried to gobble up a simple scripting language. If you are not really accustomed to any of .net stacks, fret not. All of these are totally doable in any other tech stack you will possibly prefer to use. Since ANTLR has targets for multiple languages, api can be built in basically any language and even for our sample storage solution azure document db here, you have access to multiple client SDKs.

The data:

As I mentioned above, we have a little data set of 10 entries. I’m posting a sample entry here so the rest of the tutorial makes sense. One entry points to a single tweet made by a sample user.

{
  "entities": {
    "user_mentions": [
      {
        "indices": [
          3,
          15
        ],
        "id_str": "178253493",
        "screen_name": "mikalabrags",
        "name": "Mika Labrague",
        "id": 178253493
      }
    ],
    "urls": [],
    "hashtags": [ "#Confused" ]
  },
  "in_reply_to_screen_name": null,
  "text": "RT @mikalabrags: Bipolar weather #Confused",
  "id_str": "210621130703245313",
  "place": null,
  "retweeted_status": {
    "entities": {
      "user_mentions": [],
      "urls": [],
      "hashtags": []
    },
    "in_reply_to_screen_name": null,
    "text": "Bipolar weather",
    "id_str": "210619512855343105",
    "place": null,
    "in_reply_to_status_id": null,
    "contributors": null,
    "retweet_count": 0,
    "favorited": false,
    "truncated": false,
    "source": "http://ubersocial.com",
    "in_reply_to_status_id_str": null,
    "created_at": "Thu Jun 07 06:29:39 +0000 2012",
    "in_reply_to_user_id_str": null,
    "in_reply_to_user_id": null,
    "user": {
      "lang": "en",
      "profile_background_image_url": "http://a0.twimg.com/profile_background_images/503549271/tumblr_m25lrjIjgT1qb6nmgo1_500.jpg",
      "id_str": "178253493",
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  },
  "in_reply_to_status_id": null,
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  "retweet_count": 0,
  "favorited": false,
  "truncated": false,
  "source": "&amp;amp;amp;lt;a href=\"http://blackberry.com/twitter\" rel=\"nofollow\"&amp;amp;amp;gt;Twitter for BlackBerry®&amp;amp;amp;lt;/a&amp;amp;amp;gt;",
  "in_reply_to_status_id_str": null,
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    "created_at": "Sat May 02 13:55:49 +0000 2009",
    "screen_name": "yoursweetiethea",
    "follow_request_sent": null,
    "profile_sidebar_border_color": "91f50e",
    "protected": false,
    "url": "http://yoursweetiethea.tumblr.com",
    "default_profile": false,
    "name": "Althea Arellano",
    "is_translator": false,
    "show_all_inline_media": false,
    "verified": false,
    "profile_use_background_image": true,
    "followers_count": 306,
    "profile_image_url": "http://a0.twimg.com/profile_images/2276155427/photo_201_1_normal.jpg",
    "id": "37200018",
    "profile_background_image_url_https": "https://si0.twimg.com/profile_background_images/542537222/534075_10150809727636812_541871811_10087628_844237475_n_large.jpg",
    "utc_offset": 28800,
    "friends_count": 297,
    "profile_text_color": "fa3c6b",
    "location": "Christian's Heart"
  },
  "retweeted": false,
  "id": "210621130703245300",
  "coordinates": null,
  "geo": null
}

The ANTLR grammar:

The ANTLR grammar we are going to use here is:

grammar Search;

expr: term (op term)*;
term: exactText | hashText | toText | fromText;
op: AND | OR;

toText: 'to:'ID;
fromText: 'from:'ID;
hashText: '#'ID;
exactText: EXACTTEXT;

// lexer rule
EXACTTEXT: '"' ~'"'* '"';
OR: 'OR';
AND: 'AND';
ID: [a-zA-Z_] [a-zA-Z0-9_]*;
WS: [ \n\t\r]+ -> skip;

It’s a small and simple grammar like the one we used in our scripting language tutorial. Since our REST resource query is essentially an expression, our entry rule will be expr. The railroad diagram for expr looks like:

expr_rest
Rule expr

This means we can have a single search term or multiple search term chained by a op  rule. The op rule is nothing but the two relational operator we support: AND and OR.

relop
Rule op

Along side of the op rule we also need to know what the term rule stands for. The term rule stands for the search term format we are allowed to use. In our sample query stated above, we have terms like  from:terminatorto:robocop, “I’m back” or a hashtag like #HastaLaVista. That’s why we have 4 rules defining all of these cases and the term rule is a OR relationship between them.

term_rest
rule term

I’m not going to post the railroad diagram for toText, fromText, hashText and exactText rules since they are pretty self-explanatory if you have a cursory look at the grammar.

So, what are we waiting for? Let’s start writing our little codebase that will parse this query string and translate it to an azure document db SQL that we can use to fetch the tweets. For that, we need a small repository that will connect to our desired database and collection in document db and will let us fetch some items. I only added methods that will allow us to read the tweets and connect to the database. I ignored the rest since you can always have a look at those in the quick start for azure document db.

Here’s our small rough database repository. Please remember to keep your endpoint and key strings somewhere secret and safe in production environment, since this is a tutorial, I went with what is easy and fast to go for a proof of concept.

namespace TweetQuery.Lib
{
    using System;
    using System.Collections.Generic;
    using System.Threading.Tasks;
    using Microsoft.Azure.Documents.Client;
    using Microsoft.Azure.Documents.Linq;

    public class CosmosDBRepository<T> where T : class
    {
        private readonly string Endpoint = "https://tweet.documents.azure.com:443/";
        private readonly string Key = "fjp9Z3qKPxSOfE0KS1aaKvUY27B8IoL347sdtMBMjkCQqPmoaKjGXoyltrItNXNN6h4QjAYLSY5nyb2djWWUOQ==";
        private readonly string DatabaseId = "tweetdb";
        private readonly string CollectionId = "tweets";
        private DocumentClient client;

        public CosmosDBRepository()
        {
            client = new DocumentClient(new Uri(Endpoint), Key);
        }

        public async Task<IEnumerable<T>> GetItemsAsync(string sql)
        {
            if (string.IsNullOrEmpty(sql))
                throw new ArgumentNullException(nameof(sql));

            FeedOptions queryOptions = new FeedOptions { MaxItemCount = -1 };

            var query = this.client.CreateDocumentQuery<T>(
                 UriFactory.CreateDocumentCollectionUri(DatabaseId, CollectionId),
                 sql, queryOptions)
                 .AsDocumentQuery();

            List<T> results = new List<T>();
            while (query.HasMoreResults)
            {
                results.AddRange(await query.ExecuteNextAsync<T>());
            }

            return results;
        }
    }
}

I opted for executing a SQL instead of a linq expression since constructing SQL is easier and simpler for a tutorial. Plus it decouples the query structure from compile time POCOs that we use as our models too.

I created a DocumentDbListener class based off the SearchBaseListener which was auto-generated from our ANTLR grammar. The sole purpose of this class is to generate a simple SQL against our search expression. To search inside nested arrays, I used a user defined function for azure document db. All of these are very crudely written, so forgive my indecency. Since this is just a tutorial, I tried to keep it as simple as possible.

function matchArrayElement(array, match) {
    for (var index = 0; index < array.length; index++) {
        var element = array[index];

        if (typeof match === "object") {
            for (var key in match) {
                if (match.hasOwnProperty(key) && element.hasOwnProperty(key)) {
                    var matchVal = match[key];
                    var elemVal = element[key];

                    return matchVal == elemVal;
                }
            }
        }
        else {
            return (element == match)
        }
    }

    return false;
}

All this method does is it tries to find nested array elements based on the match we send back. You can achieve the same result thorough JOINs in Azure Document Db or Array method ARRAY_CONTAINS, but I preferred a user defined function since it serves my purpose easily.

Constructing SQL from the query expression:

To understand how the SQL is generated from the query expression, let’s begin with the to:UserAccount expression. Since we start with the rule expr, let’s override the SearchBaseListener method EnterExpr first.

namespace TweetSearch.CosmosDb.DocumentDb
{
    using Antlr4.Runtime.Misc;
    using TweetSearch.CosmosDb.Util;

    public class DocumentDbListener : SearchBaseListener
    {
        private string projectionClause = "SELECT * FROM twt";
        private string whereClause;

        public string Output
        {
            get { return projectionClause + " " + whereClause; }
        }

        public override void EnterExpr([NotNull] SearchParser.ExprContext context)
        {
            this.whereClause = "WHERE";
        }
    }
}

The approach I took here is essentially the simplest. I handle the events fired the moment ANTLR enters a specific rule and I keep appending the SQL string to the whereClause. Since, entering the expr rule means that I will need a where SQL clause, I initialized it with “WHERE”. The thing to notice here is instead of concatenating I chose to initialize it because I expect this event to be fired exactly once since that is how the grammar is designed.

Following the same trail the next thing to handle will be the EnterTerm event. But, term is nothing but an OR relationship between 4 other rules. Handling them specifically gives me the edge since they produce simpler and smaller readable methods. For example, if we want to handle the to:UserAccount expression, a simple method like following should be sufficient for our use case.

        public override void EnterToText([NotNull] SearchParser.ToTextContext context)
        {
            var screenName = context.GetText().Substring(3).Enquote();
            this.whereClause = string.Concat(whereClause, " ", $"udf.matchArrayElement(twt.entities.user_mentions, {{ \"screen_name\" : {screenName} }} )");
        }

This is where our user defined function also comes in play though. I’m trying to find any tweet that has an user mention to the parsed user account I fetched from the query.

By following the same rule I completed the rest of the four rules and my full listener class looks like:

namespace TweetSearch.CosmosDb.DocumentDb
{
    using Antlr4.Runtime.Misc;
    using TweetSearch.CosmosDb.Util;

    public class DocumentDbListener : SearchBaseListener
    {
        private string projectionClause = "SELECT * FROM twt";
        private string whereClause;

        public string Output
        {
            get { return projectionClause + " " + whereClause; }
        }

        public override void EnterExpr([NotNull] SearchParser.ExprContext context)
        {
            this.whereClause = "WHERE";
        }

        public override void EnterFromText([NotNull] SearchParser.FromTextContext context)
        {
            var screenName = context.GetText().Substring(5).Enquote();
            this.whereClause = string.Concat(whereClause, " ", "twt.user.screen_name = ", screenName);
        }

        public override void EnterOp([NotNull] SearchParser.OpContext context)
        {
            var text = context.GetText();
            this.whereClause = string.Concat(this.whereClause, " ", text.ToUpper());
        }

        public override void EnterToText([NotNull] SearchParser.ToTextContext context)
        {
            var screenName = context.GetText().Substring(3).Enquote();
            this.whereClause = string.Concat(whereClause, " ", $"udf.matchArrayElement(twt.entities.user_mentions, {{ \"screen_name\" : {screenName} }} )");
        }

        public override void EnterHashText([NotNull] SearchParser.HashTextContext context)
        {
            var hashtag = context.GetText().Enquote();
            this.whereClause = string.Concat(whereClause, " ", $"udf.matchArrayElement(twt.entities.hashtags, {hashtag})");
        }

        public override void EnterExactText([NotNull] SearchParser.ExactTextContext context)
        {
            var text = context.GetText();
            this.whereClause = string.Concat(whereClause, " ", $"CONTAINS(twt.text, {text})");
        }
    }
}

We got our listener ready! Now, all we need is a context class that will bootstrap the lexer and parser and tokens so the input expression is transpiled and the output SQL is generated. Just like our last work on ANTLR, the TweetQueryContext class will look like the following:

namespace TweetSearch.CosmosDb.DocumentDb
{
    using Antlr4.Runtime;
    using Antlr4.Runtime.Tree;

    public class TweetQueryContext
    {
        private DocumentDbListener listener;

        public TweetQueryContext()
        {
            this.listener = new DocumentDbListener();
        }

        public SearchParser.ExprContext GenerateAST(string input)
        {
            var inputStream = new AntlrInputStream(input);
            var lexer = new SearchLexer(inputStream);
            var tokens = new CommonTokenStream(lexer);
            var parser = new SearchParser(tokens);
            parser.ErrorHandler = new BailErrorStrategy();

            return parser.expr();
        }

        public string GenerateQuery(string inputText)
        {
            var astree = this.GenerateAST(inputText);
            ParseTreeWalker.Default.Walk(listener, astree);
            return listener.Output;
        }
    }
}

Whew! That was easy, right?

Bootstrapping the api layer:

We have all we need except the api. Thanks to asp .net core, that is two clicks away. Open Visual Studio and open a .net core api project. Our TweetsController class looks like the following:

namespace TweetQuery.Controllers
{
    using Microsoft.AspNetCore.Mvc;
    using System.Threading.Tasks;
    using TweetQuery.Lib;
    using TweetQuery.Lib.Model;
    using TweetSearch.CosmosDb.DocumentDb;

    [Route("api/[controller]")]
    public class TweetsController : Controller
    {
        private CosmosDBRepository<Tweet> repository;
        private TweetQueryContext context;

        public TweetsController(CosmosDBRepository<Tweet> repository)
        {
            this.repository = repository;
            this.context = new TweetQueryContext();
        }

        [HttpGet("search")]
        public async Task<IActionResult> Search([FromQuery] string q)
        {
            if (string.IsNullOrEmpty(q))
                return BadRequest();

            var querySql = this.context.GenerateQuery(q).Trim();
            var result = await repository.GetItemsAsync(querySql);
            return Ok(result);
        }
    }
}

I reused the db repository we created earlier and as you see that is dependency injected in the controller which you have to configure in the ConfigureServices method in your Startup  class. I’m not adding that specific code here since it is already in the sample code and doesn’t belong to the scope of this tutorial. Same goes for the model class Tweet and the classes it uses inside.

Time to test!

The project is hosted here in github. Clone the code, and build and run it from your visual studio. As a sample query try the following:

http://localhost:5000/api/tweets/search?q=to:hatena_sugoi AND from:maeta_tw OR %23HopesUp OR "Surely June is a summer"

 

I url-encoded the hashtag here just to be nice on the REST client you might use. I highly suggest Postman if you don’t want anything heavy.

I only took the minimalists way of using ANTLR here, you can build your own expression tree based on the auto-generated listener and can do so much more if you want. A perfect example will be LinqToQuerystring written by Pete Smith. It generates the necessary LINQ expression for any IQueryable and thus allows you to write database agnostic Odata driven queries and it’s tons faster and lighter than the one Microsoft ships.

I hope this was fun. Happy RESTing!

I thought I knew C# : Garbage collection & disposal. Part-II

Okay, we are back in to collect “garbage” properly. If you haven’t the first part of this post you might want to read it here. We are following norms and conventions from the first post here too.

 Starting things from where we left off:

This write up continues after the first one where we ended things with SafeHandle class and IDisposable  interface. I do see how intriguing this is of course. But before that, like a unexpected, miserable commercial break before the actual stuff comes on your TV, let’s go have a look on something called finalization.

What you create, you “might” need to destroy:

If you know a little bit of C++ you’d know that there is a thing called destructor and the name implies it does the exactly opposite of what a constructor does. C++ kind of needs it since it doesn’t really have a proper garbage collection paradigm. But that also raises the question of whether C# has a destructor and if it does what does it do and why do we even have one since we said managed resourced would be collected automatically.

Lets jump onto some code, shall we?

class Cat
{
    ~Cart()
    {
        // cleanup things
    }
}

Wait a minute, now this is getting confusing. I have IDisposable::Dispose() and I have a destructor. Both looks like to have same responsibility. Not exactly. Before we confuse ourselves more, lets dig a bit more in the code. To be honest, C# doesn’t really have a destructor, it’s basically a syntactic sugar and inside c sharp compiler translates this segment as

protected override void Finalize()
{
    try
    {
        // Clean things here
    }
    finally
    {
        base.Finalize();
    }
}

We can pick up to things here. First thing is the destructor essentially translates to a overridden Finalize() method. And it also calls the Finalize() base class in the finally block. So, it’s essentially a recursive finalize(). Wait, we have no clue what is Finalize().

Finalize, who art thou?

Finalize is somebody who is blessed to talk with the garbage collector. If you have already questioning where the topics we discussed in last post comes in help, this is the segment. Lets start getting answers for all our confusions on last segment. First, why Finalize is overridden and where does it gets its signature. Finalize gets its signature from Object class. Okay, understandable. What would be the default implementation then? Well, it doesn’t have a default implementation. You might ask why. A little secret to slip in here, remember the mark step of garbage collector. He marks a type instance for finalization if and only if it has overridden the Finalize() method. It’s your way of telling the garbage collector that you need to do some more work before you can reclaim my memory. Garbage collector would mark this guy and put him in a finalization queue which is essentially a list of objects whose finalization codes must be run before GC can reclaim their memory. So, you are possibly left with one more confusion now. And that is, you understood garbage collector uses this method to finalize things, i.e. doing the necessary cleanup. Now, then why do we need IDisposable where we can just override finalize and be done with it, right?

Dispose, you confusing scum!

Turns out dispose intends to point out a pattern in code that you can use to release unmanaged memory. What I didn’t tell you about garbage collector before is you don’t really know when he would essentially do that, you practically have no clue. That also means if you write an app that uses unmanaged resources like crazy like reading 50-60 files at a time, you are using a lot of scarce resources at the same time. And in the end you are waiting for GC to do his job but that guy has no time table. So, hoarding these resources is not a good idea in the meantime. Since releasing unmanaged resources are developers duty, putting that over a finalize method  and waiting for GC to come and invoke that is a stupid way to go. Moreover, if you send an instance to a finalization queue, it means, GC will essentially do the memory cleanup in the next round, he will only invoke finalize this round. That also means GC has to visit you twice to clear off your unused occupied memory which you kinda need now. And the fact that you might want to release the resources NOW is a pretty good reason itself to not wait for GC to do your dirty work. And, when you actually shut down your application, Mr. GC visits you unconditionally and takes out EVERYONE he finds. I hope the need of Dispose() is getting partially clear to you now. We need a method SomeMethod() that we can call which would clean up the unmanaged resources. If we fail to call that at some point, just to make sure garbage collector can call that we will use that same method inside Finalize() so it is called anyhow. If I have not made a fool out of myself at this point, you have figured out the fact that SomeMethod() is Dispose(). Okay, so we know what we are going to do. Now we need to act on it. We will implement the dispose pattern we have been talking about. The first thing we would do here is we would try to write code that reads a couple of lines from a file. We would try to do it the unmanaged way, then move it to slowly to the managed way and in the process we would see how we can use IDisposable there too.

Doing things the unsafe way:

I stole some code from a very old msdn doc which describes a FileReader class like the following:

using System;
using System.Runtime.InteropServices;
public class FileReader
{
    const uint GENERIC_READ = 0x80000000;
    const uint OPEN_EXISTING = 3;
    IntPtr handle;
[DllImport("kernel32", SetLastError = true)]
    static extern unsafe IntPtr CreateFile(
            string FileName,                    // file name
            uint DesiredAccess,                 // access mode
            uint ShareMode,                     // share mode
            uint SecurityAttributes,            // Security Attributes
            uint CreationDisposition,           // how to create
            uint FlagsAndAttributes,            // file attributes
            int hTemplateFile                   // handle to template file
            );
[DllImport("kernel32", SetLastError = true)]
    static extern unsafe bool ReadFile(
            IntPtr hFile,                       // handle to file
            void* pBuffer,                      // data buffer
            int NumberOfBytesToRead,            // number of bytes to read
            int* pNumberOfBytesRead,            // number of bytes read
            int Overlapped                      // overlapped buffer
            );
[DllImport("kernel32", SetLastError = true)]
    static extern unsafe bool CloseHandle(
            IntPtr hObject   // handle to object
            );
    public bool Open(string FileName)
    {
        // open the existing file for reading
        handle = CreateFile(
                FileName,
                GENERIC_READ,
                0,
                0,
                OPEN_EXISTING,
                0,
                0);
if (handle != IntPtr.Zero)
            return true;
        else
            return false;
    }
    public unsafe int Read(byte[] buffer, int index, int count)
    {
        int n = 0;
        fixed (byte* p = buffer)
        {
            if (!ReadFile(handle, p + index, count, &amp;n, 0))
                return 0;
        }
        return n;
    }
    public bool Close()
    {
        // close file handle
        return CloseHandle(handle);
    }
}

Please remember you need to check your Allow Unsafe Code checkbox in your build properties before you start using this class. Lets have a quick run on the code pasted here. I don’t intend to tell everything in details here because that is not the scope of this article. But we will build up on it, so we need to know a little bit. The DllImport attribute here is essentially something  you would need to use an external dll (thus unmanaged) and map the functions inside it to your own managed class. You can also see that’s why we have used the extern keyword here. The implementations of these methods doesn’t live in your code and thus your garbage collector can’t take responsibility of clean up here. 🙂 The next thing you would notice is the fixed statement. fixed statement essentially link up a managed type to an unsafe one and thus make sure GC doesn’t move the managed type when it collects. So, the managed one stays in one place and points to the unmanaged resource perfectly. So, what are we waiting for? Lets read a file.

static int Main(string[] args)
{
    if (args.Length != 1)
    {
        Console.WriteLine("Usage : ReadFile &lt;FileName&gt;");
        return 1;
    }
    if (!System.IO.File.Exists(args[0]))
    {
        Console.WriteLine("File " + args[0] + " not found.");
        return 1;
    }
    byte[] buffer = new byte[128];
    FileReader fr = new FileReader();
    if (fr.Open(args[0]))
    {
        // Assume that an ASCII file is being read
        ASCIIEncoding Encoding = new ASCIIEncoding();
        int bytesRead;
        do
        {
            bytesRead = fr.Read(buffer, 0, buffer.Length);
            string content = Encoding.GetString(buffer, 0, bytesRead);
            Console.Write("{0}", content);
        }
        while (bytesRead &gt; 0);
        fr.Close();
        return 0;
    }
    else
    {
        Console.WriteLine("Failed to open requested file");
        return 1;
    }
}

So, this is essentially a very basic console app and looks somewhat okay. I have created a byte array of size 128 which I would use as a buffer when I read. FileReader returns 0 when it can’t read anymore. Don’t get confused seeing this.

while (bytesRead > 0)

It’s all nice and dandy to be honest. And it works too. Invoke the application (in this case the name here is  TestFileReading.exe) like the following:

TestFileReading.exe somefile.txt

And it works like a charm. But what I did here is we closed the file after use. What if something happens in the middle, something like the file not being available. Or I throw an exception in the middle. What will happen is the file would not be closed up until my process is not closed. And the GC will not take care of it because it doesn’t have anything in the Finalize() method.

Making it safe:

public class FileReader: IDisposable
{
    const uint GENERIC_READ = 0x80000000;
    const uint OPEN_EXISTING = 3;
    IntPtr handle = IntPtr.Zero;
    [DllImport("kernel32", SetLastError = true)]
    static extern unsafe IntPtr CreateFile(
            string FileName,                 // file name
            uint DesiredAccess,             // access mode
            uint ShareMode,                // share mode
            uint SecurityAttributes,      // Security Attributes
            uint CreationDisposition,    // how to create
            uint FlagsAndAttributes,    // file attributes
            int hTemplateFile          // handle to template file
            );
    [DllImport("kernel32", SetLastError = true)]
    static extern unsafe bool ReadFile(
            IntPtr hFile,                   // handle to file
            void* pBuffer,                 // data buffer
            int NumberOfBytesToRead,      // number of bytes to read
            int* pNumberOfBytesRead,     // number of bytes read
            int Overlapped              // overlapped buffer
            );
    [DllImport("kernel32", SetLastError = true)]
    static extern unsafe bool CloseHandle(
            IntPtr hObject   // handle to object
            );
    public bool Open(string FileName)
    {
        // open the existing file for reading
        handle = CreateFile(
                FileName,
                GENERIC_READ,
                0,
                0,
                OPEN_EXISTING,
                0,
                0);
        if (handle != IntPtr.Zero)
            return true;
        else
            return false;
    }
    public unsafe int Read(byte[] buffer, int index, int count)
    {
        int n = 0;
        fixed (byte* p = buffer)
        {
            if (!ReadFile(handle, p + index, count, &amp;n, 0))
                return 0;
        }
        return n;
    }
    public bool Close()
    {
        // close file handle
        return CloseHandle(handle);
    }
    public void Dispose()
    {
        if (handle != IntPtr.Zero)
            Close();
    }
}

Now, in our way towards making things safe, we implemented IDisposable here. That exposed Dispose() and the first thing I did here is we checked whether the handle is IntPtr.Zero and if it’s not we invoked Close(). Dispose() is written this way because it should be invokable in any possible time and it shouldn’t throw any exception if it is invoked multiple times. But is it the solution we want? Look closely. We wanted to have a Finalize() implementation that will essentially do the same things if somehow Dispose() is not called. Right?

Enter the Dispose(bool) overload. We want the parameter less Dispose() to be used by only the external consumers. We would issue a second Dispose(bool) overload where the boolean parameter indicates whether the method call comes from a Dispose method or from the finalizer. It would be true if it is invoked from the parameter less Dispose() method.

With that in mind our code would eventually be this:

    public class FileReader: IDisposable
    {
        const uint GENERIC_READ = 0x80000000;
        const uint OPEN_EXISTING = 3;
        IntPtr handle = IntPtr.Zero;
        private bool isDisposed;

        SafeHandle safeHandle = new SafeFileHandle(IntPtr.Zero, true);

        [DllImport("kernel32", SetLastError = true)]
        static extern unsafe IntPtr CreateFile(
              string FileName,                  // file name
              uint DesiredAccess,              // access mode
              uint ShareMode,                 // share mode
              uint SecurityAttributes,       // Security Attributes
              uint CreationDisposition,     // how to create
              uint FlagsAndAttributes,     // file attributes
              int hTemplateFile           // handle to template file
              );

        [DllImport("kernel32", SetLastError = true)]
        static extern unsafe bool ReadFile(
             IntPtr hFile,                // handle to file
             void* pBuffer,              // data buffer
             int NumberOfBytesToRead,   // number of bytes to read
             int* pNumberOfBytesRead,  // number of bytes read
             int Overlapped           // overlapped buffer
             );

        [DllImport("kernel32", SetLastError = true)]
        static extern unsafe bool CloseHandle(
              IntPtr hObject   // handle to object
              );

        public bool Open(string FileName)
        {
            // open the existing file for reading
            handle = CreateFile(
                  FileName,
                  GENERIC_READ,
                  0,
                  0,
                  OPEN_EXISTING,
                  0,
                  0);

            if (handle != IntPtr.Zero)
                return true;
            else
                return false;
        }

        public unsafe int Read(byte[] buffer, int index, int count)
        {
            int n = 0;
            fixed (byte* p = buffer)
            {
                if (!ReadFile(handle, p + index, count, &amp;n, 0))
                    return 0;
            }
            return n;
        }

        public bool Close()
        {
            // close file handle
            return CloseHandle(handle);
        }

        public void Dispose()
        {
            Dispose(true);
            GC.SuppressFinalize(this);
        }

        protected virtual void Dispose(bool isDisposing)
        {
            if (isDisposed)
                return;

            if (isDisposing)
            {
                safeHandle.Dispose();
            }

            if (handle != IntPtr.Zero)
                Close();

            isDisposed = true;
        }
    }

Now if you focus on the changes we made here is introducing the following method:

protected virtual void Dispose(bool isDisposing)

Now, this method envisions what we discussed a moment earlier. You can invoke it multiple times without any issue. There are two prominent block here.

  • The conditional block is supposed to free managed resources (Read invoking Dispose() methods of other IDisposable member/properties inside the class, if we have any.)
  • The non-conditional block frees the unmanaged resources.

You might ask why the conditional block tries to dispose managed resources. The GC takes care of that anyway right? Yes, you’re right. Since garbage collector is going to take care of the managed resources anyway, we are making sure the managed resources are disposed on demand if only someone calls the parameter less Dispose(). 

Are we forgetting something again? Remember, you have unmanaged resources and if somehow the Dispose() is not invoked you still have to make sure this is finalized by the garbage collector. Let’s write up a one line destructor here.

 ~FileReader()
 {
    Dispose(false);
 }

It’s pretty straightforward and it complies with everything we said before. Kudos! We are done with FileReader.

Words of Experience:

Although we are safe already. We indeed forgot one thing. If we invoke Dispose() now it will dispose unmanaged and managed resources both. That also means when the garbage collector will come to collect he will see there is a destructor ergo there is a Finalize() override here. So, he would still put this instance into Finalization Queue. That kind of hurts our purpose. Because we wanted to release memory as soon as possible. If the garbage collector has to come back again, that doesn’t really make much sense. So, we would like to suppress the garbage collector to invoke Finalize() if we know we have disposed it ourselves. And a single line modification to the Dispose() method would allow you to do so.

public void Dispose()
{
    Dispose(true);
    GC.SuppressFinalize(this);
}

We added the following statement to make sure if we have done disposing ourselves the garbage collector would not invoke Finalize() anymore.

GC.SuppressFinalize(this);

Now, please keep in mind that you shouldn’t write a GC.SupperssFinalize() in a derived class since your dispose method would be overridden and you would follow the same pattern and call base.Dispose(isDisposing) in the following way:

class DerivedReader : FileReader
{
   // Flag: Has Dispose already been called?
   bool disposed = false;

   // Protected implementation of Dispose pattern.
   protected override void Dispose(bool disposing)
   {
      if (disposed)
         return; 

      if (disposing) {
         // Free any other managed objects here.
         //
      }

      // Free any unmanaged objects here.
      //
      disposed = true;

      // Call the base class implementation.
      base.Dispose(disposing);
   }

   ~DerivedClass()
   {
      Dispose(false);
   }
}

It should be fairly clear to now why we are doing it this way. We want disposal to go recursively to base class. So, when we dispose the derived class resources, the base class disposes its own resources too.

To use or not to use a Finalizer:

We are almost done, really, we are. This is a section where Im supposed to tell you why you really shouldn’t use unmanaged resources whenever you need. It’s always a good idea not to write a Finalizer if you really really don’t need it. Currently we need it because you are using a unsafe file handle and we need to close it manually. To keep destructor free and as managed as possible, we should always wrap our handles in SafeHandle class and dispose the SafeHandle as a managed resource. Thus, eliminating the need for cleaning unmanaged resources and the overloaded Finalize() . You will find more about that here.

“Using” it right:

Before you figure out why I quoted the word using here, let’s finally wrap our work up. We have made our FileReader class disposable and we would like to invoke dispose() after we are done using it. We would opt for a try-catch-finally block to do it and will dispose the resources in the finally block.

FileReader fr = new FileReader();
try
{
    if (fr.Open(args[0]))
    {
        // Assume that an ASCII file is being read
        ASCIIEncoding Encoding = new ASCIIEncoding();
        int bytesRead;
        do
        {
            bytesRead = fr.Read(buffer, 0, buffer.Length);
            string content = Encoding.GetString(buffer, 0, bytesRead);
            Console.Write("{0}", content);
        }
        while (bytesRead &gt; 0);
        return 0;
    }
    else
    {
        Console.WriteLine("Failed to open requested file");
        return 1;
    }
}
finally
{
    if (fr != null)
        fr.Dispose();
}

The only difference you see here that we don’t explicitly call Close() anymore. Because that is already handled when we are disposing the FileReader instance.

Good thing for you is that C# has essentially made things even easier than this. Remember the using statements we used in Part-I? An using statement is basically a syntactic sugar placed on a try-finally block with a call to Dispose() in the finally block just like we wrote it here. Now, with that in mind, our code-block will change to:

using (FileReader fr = new FileReader())
{
    if (fr.Open(args[0]))
    {
        // Assume that an ASCII file is being read
        ASCIIEncoding Encoding = new ASCIIEncoding();
        int bytesRead;
        do
        {
            bytesRead = fr.Read(buffer, 0, buffer.Length);
            string content = Encoding.GetString(buffer, 0, bytesRead);
            Console.Write("{0}", content);
        }
        while (bytesRead &gt; 0);
        return 0;
    }
    else
    {
        Console.WriteLine("Failed to open requested file");
        return 1;
    }
}

Now you can go back to part-I and try to understand the first bits of code we saw. I hope it would make a bit better sense to you now. Hope you like it. Hopefully, if there’s a next part, I would talk about garbage collection algorithms.

You can find the code sample over github here.

I thought I knew C# : Garbage collection & disposal. Part-I

Yeah, that’s right, time to be a garbage guy. And if this line didn’t make you laugh, you probably know how bad of a stand up comedian I ever would make.

How things goes when anyone asks about these:

Now this is going to be long. And I’m going to jump into what I want to talk about right away. Let’s start with a regular Joe who writes C#. Let’s tell him to write a “safe” looking block of code that would essentially open a gzip file, read it as byte array, decompress the byte array using a buffer, write it over a memory stream and return it when he is done doing the whole thing.

This is something you might expect in return. This would be the block where the aforementioned byte array gets decompressed:

    public class Solution
    {
        public static byte[] Decompress(byte[] gzip)
        {
            using (MemoryStream memoryStream = new MemoryStream())
            using (GZipStream gzipStream = new GZipStream(memoryStream, CompressionMode.Decompress))
            {
                const int size = 4096;
                byte[] buffer = new byte[size];
                using (MemoryStream writeStream = new MemoryStream())
                {
                    int count = 0;
                    do
                    {
                        count = gzipStream.Read(buffer, 0, count);
                        if (count &amp;amp;amp;amp;amp;gt; 0)
                        {
                            memoryStream.Write(buffer, 0, count);
                        }
                    } while (count &amp;amp;amp;amp;amp;gt; 0);
                    return writeStream.ToArray();
                }
            }
        }
    }

And this would be a possible segment where you see the file being read:

    class Program
    {
        const int _max = 200000;
        static void Main()
        {
            byte[] array = File.ReadAllBytes("Capture.7z");
            Solution.Decompress(array);
        }
    }

And yes, the code sample credit goes to dotnetperls . The reason I started with an example before any explanations is I want to build up on this. I believe when you have a place to go, sometimes the fact that you know where you would end up reinforces what you will learn in the process. But it is very important here to know why you would end up here.

Breaking the code bits:

Let’s start breaking the code up into bits. Now, the first question you would ask the regular Joe like me is how do you claim this code block is “safe” and what do you mean when you say this is “safe”. The first answer would essentially be that there is a beautiful using block there which would essentially dispose the resources when it is done being used.

I focused three words here, throughout this articles I would bold out words like this and whenever I do that, if you don’t know exactly what I’m talking about, put these words in a dictionary in your brain and in turns I will explain all these. That also means if you think you know all of these, your journey ends here.

Words of wisdom:

The first word of wisdom here is dispose. And I would essentially start with some basics for it. Before going into dispose, we need to dive back on some proper backgrounds on .net garbage collection process. If you are totally new to this, garbage collector is an automatic memory manager, it lets you develop your application without having the need to free the memory every time. If I drove you inside more confusion, that means you need to know what happens when you allocate some memory, essentially which happens every time you declare and initiate any value or reference you write when you are writing C#.

Every time you write the new keyword to initialize an object in C#, you essentially allocate the object in a managed heap. And garbage collector automatically deallocates the objects that are not being used anymore from the managed heap “some time in the future”.  If you are already curious what is a managed heap, fret not. I will explain that too. But before that, lets talk about some fundamentals on memory.  When we essentially write C#, we essentially use a virtual address space. Since you have a lot of processes in the same computer who shares the same memory (read your RAM here) and you would essentially need them not to overlap with one another. Each process then needs to address a specific set of the memory for them and thus you have your virtual address space mapped for each process. By default 32 bit computers has around 2GB user mode virtual address space. When you are actually allocating memory, you allocate memory on this virtual address space, not the physical memory. For this, the garbage collector works on this virtual space and frees up this virtual memory for you automatically. Neat, huh?

I need memory:

What actually happens when we essentially write something like the following:

    static void Main()
    {
        Cat cat = new Cat("Nerd");
    }

Looks like we are initiating a harmless cat with the name Nerd. When you compile this C Sharp compiler will generate a common intermediate language (IL/CIL) code so the JIT compiler in CLR can compile those for any possible machine configuration. You see, I said a lot of jargons, I didn’t bold them out because I’m not going to talk about them here. Now the intermediate code that is being generated here kind of looks like this:

IL_000a:        newobj instance void CilNew.Cat::.ctor (string)

It looks about right, we only care about the newobj instruction here. This specific instruction needs to do three things.

  1. Calculate the total amount of memory you require for the object.
  2. Look for space in the managed heap for space.
  3. When the object is created, return the reference to the caller and advance the next object pointer  to the next available slot on the managed heap.

Im quiet sure number 1 is very very easy to understand here. Why would we need to look for space in the managed heap then? Lets look at this first.

managed-heap

If you look at the example here, now it should be pretty clear to you what I meant. If the next object pointer doesn’t find enough space to fit the next object in, you would expect a OutOfMemoryException . This can also happen when you don’t have enough physical memory either. This picture also can mislead you. I will come to that now. You might think now Virtual address space is contiguous always. Well, it’s not. Virtual address space can be fragmented. This means that there are free blocks or holes among used blocks and the virtual memory manager has to find a big enough free block to allocate so you can instantiate your variable. So, even if you have 2GB virtual address space, this does not mean you have 2GB contiguously. If you ask the virtual memory manager for 2GB of space, it could fail due to the fact you dont have that amount of contiguous address space. But for regular explanations that picture will suffice well.

Now we know how objects are allocated and we spent some time on what is the managed heap and how objects are allocated on the managed heap. The reason we discussed about this is to make you understand why you need garbage collection and when it is triggered.

States of virtual memory:

There are three states of the virtual memory. Free state says this block of memory is available for allocation. When you request for allocation, it goes to Reserved state. Much like booking a hotel. Now your memory block is reserved for you but not used yet. And no one else can use this block either because you reserved it. When you finally use it, it goes to Committed state. In this state, the block of memory has a physical storage association.

The garbage collector kicks in:

There are definitely multiple conditions which are responsible for garbage collection. And you already know the very first one now. When you run out of space for a new allocation in the virtual address space. We are going to jump in and see what actually the garbage collector does in a very basic level.

Garbage collection happens in two stages. Mark and Sweep. The mark essentially searches for managed objects that are referenced in managed code. It will attempt to finalize objects that are unreachable. That is the first thing to do on sweep stage. The last work to do on sweep stage is to reclaim the memory of the unreachable objects now.

I know you are thinking what is managed code. We would come back to this in the journey. Don’t worry. For now, keep in your mind that garbage collector can only deal with managed code.

So, the technique is essentially to mark objects the program might be using and just clean off the rest one. But, how the garbage collector would know which objects it needs to clean? How would it decide which objects are unreachable. It does it using something called Object Graph which is not essentially under the context of this article. But I do have a nice representation to go with.

object-graph-pr-before-gc

Lets assume this is the situation in the heap. You have a managed heap like this and lets assume the garbage collector kicks in due to less memory. It would essentially look like the following after the collection.

object-graph-pr-after-gc

Now it should be evident to you what basically happens in a garbage collection from a birds eye view. Marked man needs finalization and then it leaves.

I still didn’t properly explain how you essentially get these marked objects.  To understand that properly, we need to understand about generations.

Generations inside the heap:

Generations inside the heap essentially dictates how long the object would be essentially needed. And thus it is divided into long-lived and short-lived objects. There are three generations here and the indexing starts from zero:

  1. Generation 0: This is the youngest generation and contains short-lived objects. Temporary and newly allocated objects live here. This is the part of the heap where garbage collection happens very frequently.
  2. Generation 1: This is essentially a buffer between generation 0 and generation 2. Generation 2 contains long-lived objects. Generation 1 essentially holds the objects who is still looking to be short-lived but survived generation 0.
  3. Generation 2: This is the generation of long-lived objects. These objects are usually objects that stays for long time in the process. Statics come first in mind. And a new object can be allocated straight to generation 2 instead of generation 0 if it’s really big. Like a big array with a lot of space allocation.

Garbage collections are generation specific but the collection is recursive up until the younger generation. So it clears generation 1, it would also clear generation 0. If GC clears generation 2, it would also go down to clear generation 1 and generation 0.

I used the word survive  a moment ago. What I wanted to say is if an object doesn’t get reclaimed/cleaned up during a sweep operation over a generation it gets promoted to the next generation. If survival rate is higher in a generation, GC tries to increase the threshold of allocation of that specific generation. So in the next cleanup the application gets a big size of memory freed.

One more thing to remember here. Garbage collector would stop any managed thread to work. So, it has to be quick and efficient unless you are looking at performance penalties.

Back to the code bits:

If you have survived up until now, you deserve to go back to the code bit at the beginning. The first thing I’m going to clear up is the managed vs unmanaged resources. Managed resources are directly under the control of the garbage collector. It is a result of managed code which would eventually compile to intermediate language. Unmanaged resources are resources your garbage collector don’t really know about. That includes, open files, open network connections and of course unmanaged memory. Now, if you are using C# classes to do these, most of the times these are almost managed. That means the managed code does the “dirty work” inside and you don’t have to clean up these yourselves. The garbage collector would clean up the managed wrapper and the managed wrapper would clean up the unmanaged code in the disposal process.

Now, lets go back to the word dispose here. How would I dispose something off my code. Is there a method somewhere, something I could use? Indeed there is. A dispose method implementation has essentially two variations. Since your garbage collection can’t handle unmanaged resources, you need to wrap them. The first technique is to wrap them under any class derived from SafeHandle class and use IDisposable  interface to make it properly disposable. This very interface would expose the dispose() method you need and you would use that to dispose the resources yourself.

I explained in details how to do that in the next part.

To switch or not to switch

Now, the first thing that comes to mind when you are reading the title of this blog is a very “insightful” question that potentially dictates my reluctance to learn switch properly in C# even after all these years. Fret not, I’m the same old uncool dude who learns stuff late and in the process gets his ass kicked.

Today I want to go through the age old switch statement we all have been using. There’s a good number among us who prefers switch over an annoying if-else block. And I claim no crime there, not at all. Up until a couple of days ago I was a happy chump to use switch whenever I’m handling compile time constants like enums and writing if else blocks for my logical operation checks. Simple, happy as ever. All of that changed when my skyrim-ridden dull brain asked “Why there are two prominent branching paradigm here in this statically typed language” and kind of made a good point to find that out. I don’t remember what point my brain made at that time, sorry. But it’s my brain and I can’t deny much like deadpool cant.

deadpool

Let’s stay on focus, shall we? Lets go ahead and write a simple switch snippet like the following:

    public class Program
    {
        enum WhoGivesACrap
        {
            I_do,
            Nope_I_dont,
            Maybe_NotSure
        }
        static void Main(string[] args)
        {
            WhoGivesACrap whoGivesACrap = WhoGivesACrap.I_do;

            switch(whoGivesACrap)
            {
                case WhoGivesACrap.I_do:
                    System.Console.WriteLine($"{whoGivesACrap}");
                    break;
                case WhoGivesACrap.Maybe_NotSure:
                    System.Console.WriteLine($"{whoGivesACrap}");
                    break;
                case WhoGivesACrap.Nope_I_dont:
                    System.Console.WriteLine($"{whoGivesACrap}");
                    break;
            }
        }
    }

Now, this is definitely the first day at C# programming grade code. Dull, blasphemous and quiet frankly not worth of any attention. Same thoughts as mine to be honest. Thus I kept looking, booted up ildasm and asked what we can we see inside. Now, I can post the full de-assembled MSIL code here but that won’t make much of sense. Lets look on the parts we really want to look.

//000014:
//000015:             switch(whoGivesACrap)
    IL_0003:  ldloc.0
    IL_0004:  stloc.1
    .line 16707566,16707566 : 0,0 ''
//000016:             {
//000017:                 case WhoGivesACrap.I_do:
//000018:                     System.Console.WriteLine($"{whoGivesACrap}");
//000019:                     break;
//000020:                 case WhoGivesACrap.Maybe_NotSure:
//000021:                     System.Console.WriteLine($"{whoGivesACrap}");
//000022:                     break;
//000023:                 case WhoGivesACrap.Nope_I_dont:
//000024:                     System.Console.WriteLine($"{whoGivesACrap}");
//000025:                     break;
//000026:             }
//000027:         }
//000028:     }
//000029: }
    IL_0005:  ldloc.1
    IL_0006:  switch     (
                          IL_0019,
                          IL_0049,
                          IL_0031)
    IL_0017:  br.s       IL_0061

    .line 18,18 : 21,66 ''
//000018:                     System.Console.WriteLine($"{whoGivesACrap}");

I opted for dumping with my C# source code. And the thing that catches my eye is the switch  invocation with the three jump locations. And as my enums were adjacent it makes sense. CIL switch essentially create a jump table. The three arguments it takes are essentially jump locations which will be compared against my enums. Cool, at least now I have an answer why it is different than if-else-if-else. Remember I didn’t say if-else block because it makes more sense to do if-else in this fashion than checking else for no apparent reason.

If you are still not bored enough why don’t you go and have a look here?

Now, I also thought this would be the end of it. But the voices in my head reminded me to do one more thing. And that’s using non-adjacent values. Thus I modified my enum in the following fashion.

        enum WhoGivesACrap
        {
            I_do = 17,
            Nope_I_dont = 57,
            Maybe_NotSure = 945
        }

And hooked up ildasm again to figure out what happened this time. Now, my values are non adjacent. It doesn’t really make sense anymore to create around 900+ entry jump table.

//000014:
//000015:             switch(whoGivesACrap)
    IL_0004:  ldloc.0
    IL_0005:  stloc.1
    .line 16707566,16707566 : 0,0 ''
//000016:             {
//000017:                 case WhoGivesACrap.I_do:
//000018:                     System.Console.WriteLine($"{whoGivesACrap}");
//000019:                     break;
//000020:                 case WhoGivesACrap.Maybe_NotSure:
//000021:                     System.Console.WriteLine($"{whoGivesACrap}");
//000022:                     break;
//000023:                 case WhoGivesACrap.Nope_I_dont:
//000024:                     System.Console.WriteLine($"{whoGivesACrap}");
//000025:                     break;
//000026:             }
//000027:         }
//000028:     }
//000029: }
    IL_0006:  ldloc.1
    IL_0007:  ldc.i4.s   17
    IL_0009:  beq.s      IL_001e

    IL_000b:  br.s       IL_000d

    IL_000d:  ldloc.1
    IL_000e:  ldc.i4.s   57
    IL_0010:  beq.s      IL_004e

    IL_0012:  br.s       IL_0014

    IL_0014:  ldloc.1
    IL_0015:  ldc.i4     0x3b1
    IL_001a:  beq.s      IL_0036

    IL_001c:  br.s       IL_0066

    .line 18,18 : 21,66 ''
//000018:                     System.Console.WriteLine($"{whoGivesACrap}");

And I wasn’t wrong. Since the values are non-adjacent now CSC opted for loading the enums separately and used beq.s which stands for branch-if-equal in short form. Technically this looks like a vanilla if-else-if block. Although I can’t possibly say CSC would generate opcodes following this and this only.

I know this whole thing might sound insanely boring while performance wise this would only make a minuscule difference. Still, it’s always fun to answer the voices inside my head and they are much happier when they have something to hold against when they ask why.

Until next time!

Porting your WebApi 2.2 app to Azure Service Fabric

Now, to start talking about this, you gotta know why this is spawned off instead of spawning off a “hello world” to service fabric. I’m back to writing after a long time and it’s only fitting I do share what I have been doing meanwhile.

Usually in a production scenario you’d end up with a case where you have your applications lying around in several tiers. To be honest, I’m pointing reference to a production environment which is microservices driven. If you’re reading this and questioning why I did that you probably want to go down here to get yourself started with the basic concepts of Azure Service Fabric.

Now, I hope you have familiarized yourself with service fabric by now and I can start talking about how you can port your existing IIS hosted Asp.net Web Api 2.2 application to a self-hosted statless web api service in Azure Service Fabric.

To get yourself started please install Azure Service Fabric local development environment in your machine from here and go through the set of instructions to get yourself started. When you’re done with these, you’d see Visual Studio come up with a new set of project types for service fabric.

And it would pretty much like the following:

servicefabricprojecttype

Now, before we click the that elusive OK button, we need to understand what are the tasks at hand here. We have to do two things here.

1. Make sure our web api 2.2 app is compatible with self host as it was using IIS.

2. Make sure we can salvage the same Visual Studio project we used for our web project.

Making sure the existing web api 2.2 app is compatible of self hosting:

  1. For number one, one might think it should be farely easy to port a IIS driven web api app to a self hosted app, the reality might not be that easy. First, make sure you have a OWIN startup class initiated. Usually you would have one if you are using OWIN. And you’d also need a Program.cs file which is standard one for a console application as now your web app would be  a self-hosted application. So go ahead and add these two files to your web project and if you need reference, both of them are shown here.
  2. Now, although you have a startup.cs and Program.cs initiated already, you still haven’t converted your web app project to a console application. To do so, please go over your project properties and  on the Applications section, select Output Type as Console Application and set the Program class as the Startup object. Now, if you forget to add Program.cs as startup object which you’d only be able to do after you have created that file with a static void Main(string[] args) initiated on it, as long as it is named Program.cs the project would invoke it automatically.
  3. If you are using anything from Microsoft.Owin.Host.SystemWeb library, please make sure that these wont work anymore for you. For example if you are using code segments like the following to resolve file path to your traditional web app deployment and subsidiary folders like App_data, it wont work anymore for you.
     string path = System.Web.Hosting.HostingEnvironment.MapPath(@"~/App_Data/EmailTemplates/");
    

    So for things like these you might have to resort yourself to solutions that resolves deployment path in a console application.

  4. Now, there are some other pranks too. If you are familiar with Asp.net Identity and use it for your default identity provider, you’re in for a treat. Usually when you develop a expirable token generation paradigm for email and passwords, you’d need to use a IDataProtectionProvider which is usually the MachineKeyDataProtectionProvider under the namespace of Microsoft.Owin.Host.SystemWeb.DataProtection which you’d probably ditch because you would be using owin self host/Katana now. So, expect a null there where you try something like app.GetDataProtectionProvider() where app is your IAppBuilder. I ran into this myself and thanks to Katana being open source, you just pick up the MachineKeyDataProtectionProovider class from here.Just make sure you use it as  IDataProtectionProvider like the following:
    using System;
    using System.Web.Security;
    using Microsoft.Owin.Security.DataProtection;
    
    namespace TaskCat.Lib.DataProtection
    {
        using DataProtectionProviderDelegate = Func<string[], Tuple<Func<byte[], byte[]>, Func<byte[], byte[]>>>;
        using DataProtectionTuple = Tuple<Func<byte[], byte[]>, Func<byte[], byte[]>>;
    
        ///
    <summary>
        /// Used to provide the data protection services that are derived from the MachineKey API. It is the best choice of
        /// data protection when you application is hosted by ASP.NET and all servers in the farm are running with the same Machine Key values.
        /// </summary>
    
        internal class MachineKeyDataProtectionProvider: IDataProtectionProvider
        {
            ///
    <summary>
            /// Returns a new instance of IDataProtection for the provider.
            /// </summary>
    
            /// <param name="purposes">Additional entropy used to ensure protected data may only be unprotected for the correct purposes.</param>
            /// <returns>An instance of a data protection service</returns>
            public virtual MachineKeyDataProtector Create(params string[] purposes)
            {
                return new MachineKeyDataProtector(purposes);
            }
    
            public virtual DataProtectionProviderDelegate ToOwinFunction()
            {
                return purposes =>
                {
                    MachineKeyDataProtector dataProtecter = Create(purposes);
                    return new DataProtectionTuple(dataProtecter.Protect, dataProtecter.Unprotect);
                };
            }
    
            IDataProtector IDataProtectionProvider.Create(params string[] purposes)
            {
                return this.Create(purposes);
            }
        }
    }
    

    And you’d need to do the same with MachineKeyDataProtector class and use it as a IDataProtector like the following:

    using System.Web.Security;
    using Microsoft.Owin.Security.DataProtection;
    
    namespace TaskCat.Lib.DataProtection
    {
        internal class MachineKeyDataProtector: IDataProtector
        {
            private readonly string[] _purposes;
    
            public MachineKeyDataProtector(params string[] purposes)
            {
                _purposes = purposes;
            }
    
            public virtual byte[] Protect(byte[] userData)
            {
                return MachineKey.Protect(userData, _purposes);
            }
    
            public virtual byte[] Unprotect(byte[] protectedData)
            {
                return MachineKey.Unprotect(protectedData, _purposes);
            }
        }
    }
    
    

    Then you can use it as your replacement of your old one from Microsoft.Owin.Host.SystemWeb.DataProtection. This is not really needed if you don’t use Asp.net Identity but of to good use if you land in the same problem.Now, hopefully your current web api app would be self hosted just fine if you try something like the following:

    // Start OWIN host
    using (WebApp.Start<Startup>(url: baseAddress))
    {
      Console.WriteLine("Press ENTER to stop the server and close app...");
      Console.ReadLine();
    }
    

Now, lets try salvaging that Visual Studio project as much as we can.

Project changes when it comes to Visual Studio

  1. Now, you can use the converted project but from my personal experience it would be easier for you if you just click the new ServiceFabric project button now and create a new Stateless WebApi project. You can port/reuse most of your code and as the boilerplates are already written, you don’t really have to rewrite that.
  2. If you want to keep the old project intact, you can of course reference the old project in the new stateless web api service project and use your old startup class to hook up the OwinCommunicationListener instead of the one that it comes with. But beware, nuget might make it a nightmare for you if dependencies are not met/mismatched.

Hope you guys have fun with Azure Service Fabric and if you really want to look for more how you can do you own stateless web api from scratch take a look here. If I find time, I’d write one too.