Bulk inserts in MongoDB

Like most database systems, MongoDB provides API calls that allow multiple documents to be inserted or retrieved in a single operation.

These “Array” or “Bulk” interfaces improve database performance markedly by reducing the number of round trips between the client and the databases – dramatically. To realise how fundamental an optimisation this is, consider that you have a bunch of people that you are going to take across a river. You have a boat that can take 100 people at a time, but for some reason you are only taking one person across in each trip – not smart, right? Failing to take advantage of array inserts is very similar: you are essentially sending network packets that could take hundreds of documents over with only a single document in each packet.

Optimizing bulk reads using .batchSize()

Read the rest of this post at https://medium.com/dbkoda/bulk-operations-in-mongodb-ed49c109d280

Graph Lookup in MongoDB 3.3

Specialized graph databases such as Neo4J specialize in traversing graphs of relationships – such as those you might find in a social network.  Many non-graph databases have been incorporating Graph Compute Engines to perform similar tasks.  In the MongoDB 3.3 release, we now have the ability to perform simple graph traversal using the $graphLookup aggregation framework function.  This will become a production feature in the 3.4 release.

The new feature is documented in MongoDB Jira SERVER-23725.  The basic syntax is shown here:

   1: {$graphLookup:

   2:         from: <name of collection to look up into>,

   3:         startWith: <expression>,

   4:         connectFromField: <name of field in document from “from”>,

   5:         connectToField: <name of field in document from “from”>,

   6:         as: <name of field in output document>,

   7:         maxDepth: <optional - non-negative integer>,

   8:         depthField: <optional - name of field in output

   9:  documents>

  10:     }

Please go to https://medium.com/dbkoda/optimising-graph-lookups-in-mongodb-49483afb55c8 to read the rest of this article (updated for MongoDB 3.6)

Join performance in MongoDB 3.2 using $lookup

One of the key tenants of MongoDB schema design is to account for the absence of server-side joins.  Data is joined all the time inside of application code of course, but traditionally there’s been no way to perform joins within the server itself. 

This changed in 3.2 with the introduction of the $lookup operator within the aggregation framework.  $lookup performs the equivalent of a left outer join – eg: it retrieves matching data from another document and returns null data if no match is found.

Here’s an example using the MongoDB version of the Sakila dataset that I converted from MySQL back in this post: 

Please go to https://medium.com/dbkoda/coding-efficient-mongodb-joins-97fe0627751a to read the rest of this post (it has been migrated to a medium.com article and updated). 

Sakila sample schema in MongoDB

2018 Update:  You can download this and other sample schemas we use in dbKoda from https://medium.com/dbkoda/mongodb-sample-collections-52d6a7745908.

I wanted to do some experimenting with MongoDB, but I wasn’t really happy with any of the sample data I could find in the web.  So I decided that I would translate the MySQL “Sakila” schema into MongoDB collections as part of the learning process.   

For those that don’t know, Sakila is a MySQL sample schema that was published about 8 years ago.  It’s based on a DVD rental system.   OK, not the most modern data ever, but DVDs are still a thing aren’t they??

You can get the MongoDB version of Sakilia here.  To load, use unpack using tar zxvf sakilia.tgz then use mongoimport to load the resulting JSON documents.  On windows you should be able to double click on the file to get to the JSON.  

The Sakila database schema is shown below.  There are 16 tables representing a fairly easy to understand inventory of films, staff, customers and stores.

When modelling MongoDB schemas, we partially ignore our relational modelling experience – “normalization” is not the desired end state.   Instead of driving our decision on the nature of the data, we drive it on the nature of operations.  The biggest decision is which “entities” get embedded within documents, and which get linked.  I’m not the best person to articulate these principles – the O’Reilly book “MongoDB Applied Design Patterns” does a pretty good job and this presentation is also useful.

My first shot at mapping the data – which may prove to be flawed as I play with MongoDB queries – collapsed the 16 tables into just 3 documents:  FILMS, STORES and CUSTOMERS.   ACTORS became a nested document in FILMS, STAFF and INVENTORY were nested into STORES, while RENTALS and PAYMENTS nested into CUSTOMERS.   Whether these nestings turn out to be good design decisions will depend somewhat on the application.  Some operations are going to be awkward while others will be expedited.

Here’s a look at the FILMS collection:

Here is STORES:

And here is CUSTOMERS:

Looks like I have to fix some float rounding issues on customers.rentals.payments.amount .

The code that generates the schema is here.   It’s pretty slow, mainly because of the very high number of lookups on rentals and payments.  It would be better to bulk collect everything and scan through it but it would make the code pretty ugly.   If this were Oracle I’m pretty sure I could make it run faster but with MySQL SQL tuning is much harder.

Code is pretty straight forward.  To insert a MongoDB document we get the DBCollection, then create BasicDBObjects which we insert into the DBCollection.  To nest a documnet we create a BasicDBList and insert BasicDBObjects into it.  Then we add the BasicDBList to the parent BasicDBObject.  The following snippit illustrates that sequence.  It's mostly boilerplate code, with the only human decision being the nesting structure. 

   1: DBCollection filmCollection = mongoDb.getCollection(mongoCollection);

   2:  

   3: while (fileRs.next()) { // For each film

   4:  

   5:         // Create the actors document

   6:         BasicDBObject filmDoc = new BasicDBObject();

   7:         Integer filmId = fileRs.getInt("FILM_ID");

   8:         filmDoc.put("_id", filmId);

   9:         filmDoc.put("Title", fileRs.getString("TITLE"));

  10:         // Other attributes

  11:         BasicDBList actorList = getActors(mysqlConn, filmId);

  12:         // put the actor list into the film document

  13:         filmDoc.put("Actors", actorList);

  14:         filmCollection.insert(filmDoc); // insert the film

  15:  

  16:     }

Anyway, hopefully this might be of some use to those moving from MySQL to MongoDB.  Comments welcome!

MongoDB, Oracle and Toad for Cloud Databases

We recently added support for MongoDB in Toad for Cloud Databases, so I took the opportunity of writing my first MongoDB Java program and taking the Toad functionality for a test drive.

MongoDB is a non-relational, document oriented database that is extremely popular with developers (see for instance this Hacker news poll).   Toad for cloud databases allows you to work with non-relational data using SQL by normalizing the data structures and converting SQL to the non-relational calls.

I wanted to get started by creating some MongoDB collections with familiar data.  So I wrote a Java program that takes data out of the Oracle sample schema, and loads it into Mongo as documents.  The program is here.

The key parts of the code are shown here:

   1: while (custRs.next()) { // For each customer

   2:     String custId = custRs.getString("CUST_ID");

   3:     String custFirstName = custRs.getString("CUST_FIRST_NAME");

   4:     String custLastName = custRs.getString("CUST_LAST_NAME");

   5:  

   6:     //Create the customer document 

   7:     BasicDBObject custDoc = new BasicDBObject();

   8:     custDoc.put("_id", custId);

   9:     custDoc.put("CustomerFirstName", custFirstName);

  10:     custDoc.put("CustomerLastName", custLastName);

  11:     // Create the product sales document 

  12:     BasicDBObject customerProducts = new BasicDBObject();

  13:     custSalesQry.setString(1, custId);

  14:     ResultSet prodRs = custSalesQry.executeQuery();

  15:     Integer prodCount = 0;

  16:     while (prodRs.next()) { //For each product sale 

  17:         String  timeId=prodRs.getString("TIME_ID"); 

  18:         Integer prodId = prodRs.getInt("PROD_ID");

  19:         String prodName = prodRs.getString("PROD_NAME");

  20:         Float Amount = prodRs.getFloat("AMOUNT_SOLD");

  21:         Float Quantity = prodRs.getFloat("QUANTITY_SOLD");

  22:         // Create the line item document 

  23:         BasicDBObject productItem = new BasicDBObject();            

  24:         productItem.put("prodId", prodId);

  25:         productItem.put("prodName", prodName);

  26:         productItem.put("Amount", Amount);

  27:         productItem.put("Quantity", Quantity);

  28:         // Put the line item in the salesforcustomer document 

  29:         customerProducts.put(timeId, productItem);

  30:         if (prodCount++ > 4) { // Just 5 for this demo

  31:             prodCount = 0;

  32:             break;

  33:         }

  34:     }

  35:     // put the salesforcustomer document in the customer document 

  36:     custDoc.put("SalesForCustomer", customerProducts);

  37:  

  38:     System.out.println(custDoc);

  39:     custColl.insert(custDoc);  //insert the customer 

  40:     custCount++;

  41:  

  42: }

Here’s how it works:

Lines	Description
1-4	We loop through each customer,   retrieving the key customer details
7-10	We create a basic MongoDB document that contains the customer details
12	We create another MongoDB document that will contain all the product sales for the customer
16-21	Fetching the data for an individual sale for that customer from Oracle
23-27	We create a document for that single sale
29	Add the sale to the document containing all the sales
36	Add all the sales to the customer
39	Add the customer document to the collection

The MongoDB API is very straight forward; much easier than similar APIs for HBase or Cassandra.

When we run the program, we create JSON documents in Mongo DB that look like this:

   1: { "_id" : "7" , "CustomerFirstName" : "Linette" , "CustomerLastName" : "Ingram" , 

   2:     "SalesForCustomer" : {

   3:         "2001-05-30 00:00:00" : { "prodId" : 28 , "prodName" : "Unix/Windows 1-user pack" , "Amount" : 205.76 , "Quantity" : 1.0} , 

   4:         "1998-04-18 00:00:00" : { "prodId" : 129 , "prodName" : "Model NM500X High Yield Toner Cartridge" , "Amount" : 205.48 , "Quantity" : 1.0}

   5:     }

   6: }

   7: { "_id" : "8" , "CustomerFirstName" : "Vida" , "CustomerLastName" : "Puleo" , 

   8:     "SalesForCustomer" : { 

   9:         "1999-01-27 00:00:00" : { "prodId" : 18 , "prodName" : "Envoy Ambassador" , "Amount" : 1726.83 , "Quantity" : 1.0} , 

  10:         "1999-01-28 00:00:00" : { "prodId" : 18 , "prodName" : "Envoy Ambassador" , "Amount" : 1726.83 , "Quantity" : 1.0} , 

  11:         "1998-04-26 00:00:00" : { "prodId" : 20 , "prodName" : "Home Theatre Package with DVD-Audio/Video Play" , "Amount" : 608.39 , "Quantity" : 1.0} ,

  12:         "1998-01-19 00:00:00" : { "prodId" : 28 , "prodName" : "Unix/Windows 1-user pack" , "Amount" : 216.99 , "Quantity" : 1.0} , 

  13:         "1998-03-19 00:00:00" : { "prodId" : 28 , "prodName" : "Unix/Windows 1-user pack" , "Amount" : 216.99 , "Quantity" : 1.0} 

  14:     }

  15: }

Toad for Cloud “renormalizes” the documents so that they resemble something that we might use in a more conventional database.  So in this case,   Toad creates two tables from the Mongo collection, one for customers, and one which contains the sales for a customer.   You can rename the auto-generated foreign keys and the sub-table name to make this a bit clearer, as in the example below:

We can more clearly see the relationships in the .NET client by using Toad’s visual query builder (or we could have used the database diagram tool):

MongoDB has a pretty rich query language, but it’s fairly mysterious to those of us are used to SQL, and it’s certainly not as rich as the SQL language.  Using Toad for Cloud, you can issue ANSI standard SQL against your MongoDB tables and quickly browse or perform complex queries.  Later this year,   this Mongo support will emerge in some of our commercial data management tools such as Toad for Data Analysts and our soon to be announced BI tools.