Data types supported by DynamoDB include those specific to attributes, actions, and your coding language of choice.

Attribute Data Types

DynamoDB supports a large set of data types for table attributes. Each data type falls into one of the three following categories −

• Scalar − These types represent a single value, and include number, string, binary, Boolean, and null.

• Document − These types represent a complex structure possessing nested attributes, and include lists and maps.

• Set − These types represent multiple scalars, and include string sets, number sets, and binary sets.

Remember DynamoDB as a schemaless, NoSQL database that does not need attribute or data type definitions when creating a table. It only requires a primary key attribute data types in contrast to RDBMS, which require column data types on table creation.

Scalars

• Numbers − They are limited to 38 digits, and are either positive, negative, or zero.

• String − They are Unicode using UTF-8, with a minimum length of >0 and maximum of 400KB.

• Binary − They store any binary data, e.g., encrypted data, images, and compressed text. DynamoDB views its bytes as unsigned.

• Boolean − They store true or false.

• Null − They represent an unknown or undefined state.

Document

• List − It stores ordered value collections, and uses square ([…]) brackets.

• Map − It stores unordered name-value pair collections, and uses curly ({…}) braces.

Set

Sets must contain elements of the same type whether number, string, or binary. The only limits placed on sets consist of the 400KB item size limit, and each element being unique.

Action Data Types

DynamoDB API holds various data types used by actions. You can review a selection of the following key types −

• AttributeDefinition − It represents key table and index schema.

• Capacity − It represents the quantity of throughput consumed by a table or index.

• CreateGlobalSecondaryIndexAction − It represents a new global secondary index added to a table.

• LocalSecondaryIndex − It represents local secondary index properties.

• ProvisionedThroughput − It represents the provisioned throughput for an index or table.

• PutRequest − It represents PutItem requests.

• TableDescription − It represents table properties.

Supported Java Datatypes

DynamoDB provides support for primitive data types, Set collections, and arbitrary types for Java.

Creating a table generally consists of spawning the table, naming it, establishing its primary key attributes, and setting attribute data types.

Utilize the GUI Console, Java, or another option to perform these tasks.

Create Table using the GUI Console

Create a table by accessing the console at . Then choose the “Create Table” option.

Our example generates a table populated with product information, with products of unique attributes identified by an ID number (numeric attribute). In the Create Table screen, enter the table name within the table name field; enter the primary key (ID) within the partition key field; and enter “Number” for the data type.

After entering all information, select Create.

Create Table using Java

Use Java to create the same table. Its primary key consists of the following two attributes −

• ID − Use a partition key, and the ScalarAttributeType N, meaning number.

• Nomenclature − Use a sort key, and the ScalarAttributeType S, meaning string.

Java uses the createTable method to generate a table; and within the call, table name, primary key attributes, and attribute data types are specified.

You can review the following example −

import java.util.Arrays;

import com.amazonaws.services.dynamodbv2.AmazonDynamoDBClient;
import com.amazonaws.services.dynamodbv2.document.DynamoDB;
import com.amazonaws.services.dynamodbv2.document.Table;

import com.amazonaws.services.dynamodbv2.model.AttributeDefinition;
import com.amazonaws.services.dynamodbv2.model.KeySchemaElement;
import com.amazonaws.services.dynamodbv2.model.KeyType;
import com.amazonaws.services.dynamodbv2.model.ProvisionedThroughput;
import com.amazonaws.services.dynamodbv2.model.ScalarAttributeType;

public class ProductsCreateTable {
   public static void main(String[] args) throws Exception {
      AmazonDynamoDBClient client = new AmazonDynamoDBClient()
         .withEndpoint("http://localhost:8000");

      DynamoDB dynamoDB = new DynamoDB(client);
      String tableName = "Products";
      try {
         System.out.println("Creating the table, wait...");
         Table table = dynamoDB.createTable (tableName,
            Arrays.asList (
               new KeySchemaElement("ID", KeyType.HASH), // the partition key
                                                         // the sort key
               new KeySchemaElement("Nomenclature", KeyType.RANGE)
            ),
            Arrays.asList (
               new AttributeDefinition("ID", ScalarAttributeType.N),
               new AttributeDefinition("Nomenclature", ScalarAttributeType.S)
            ),
            new ProvisionedThroughput(10L, 10L)
         );
         table.waitForActive();
         System.out.println("Table created successfully.  Status: " +
            table.getDescription().getTableStatus());

      } catch (Exception e) {
         System.err.println("Cannot create the table: ");
         System.err.println(e.getMessage());
      }
   }
}

In the above example, note the endpoint: .withEndpoint.

It indicates the use of a local install by using the localhost. Also, note the required ProvisionedThroughput parameter, which the local install ignores.

DynamoDB provides three options for performing operations: a web-based GUI console, a JavaScript shell, and a programming language of your choice.

In this tutorial, we will focus on using the GUI console and Java language for clarity and conceptual understanding.

GUI Console

The GUI console or the AWS Management Console for Amazon DynamoDB can be found at the following address −

It allows you to perform the following tasks −

• CRUD
• View Table Items
• Perform Table Queries
• Set Alarms for Table Capacity Monitoring
• View Table Metrics in Real-Time
• View Table Alarms

If your DynamoDB account has no tables, on access, it guides you through creating a table. Its main screen offers three shortcuts for performing common operations −

• Create Tables
• Add and Query Tables
• Monitor and Manage Tables

The JavaScript Shell

DynamoDB includes an interactive JavaScript shell. The shell runs inside a web browser, and the recommended browsers include Firefox and Chrome.

Note − Using other browsers may result in errors.

Access the shell by opening a web browser and entering the following address −http://localhost:8000/shell

Use the shell by entering JavaScript in the left pane, and clicking the “Play” icon button in the top right corner of the left pane, which runs the code. The code results display in the right pane.

DynamoDB and Java

Use Java with DynamoDB by utilizing your Java development environment. Operations confirm to normal Java syntax and structure.

In this chapter, we will discuss regarding how we can delete a table and also the different ways of deleting a table.

Table deletion is a simple operation requiring little more than the table name. Utilize the GUI console, Java, or any other option to perform this task.

Delete Table using the GUI Console

Perform a delete operation by first accessing the console at −

.

Choose Tables from the navigation pane, and choose the table desired for deletion from the table list as shown in the following screeenshot.

Finally, select Delete Table. After choosing Delete Table, a confirmation appears. Your table is then deleted.

Delete Table using Java

Use the delete method to remove a table. An example is given below to explain the concept better.

import com.amazonaws.services.dynamodbv2.AmazonDynamoDBClient;
import com.amazonaws.services.dynamodbv2.document.DynamoDB;
import com.amazonaws.services.dynamodbv2.document.Table;

public class ProductsDeleteTable {
   public static void main(String[] args) throws Exception {
      AmazonDynamoDBClient client = new AmazonDynamoDBClient()
         .withEndpoint("http://localhost:8000");

      DynamoDB dynamoDB = new DynamoDB(client);
      Table table = dynamoDB.getTable("Products");
      try {
         System.out.println("Performing table delete, wait...");
         table.delete();
         table.waitForDelete();
         System.out.print("Table successfully deleted.");
      } catch (Exception e) {
         System.err.println("Cannot perform table delete: ");
         System.err.println(e.getMessage());
      }
   }
}

Querying a table primarily requires selecting a table, specifying a partition key, and executing the query; with the options of using secondary indexes and performing deeper filtering through scan operations.

Utilize the GUI Console, Java, or another option to perform the task.

Query Table using the GUI Console

Perform some simple queries using the previously created tables. First, open the console at

Choose Tables from the navigation pane and select Reply from the table list. Then select the Items tab to see the loaded data.

Select the data filtering link (“Scan: [Table] Reply”) beneath the Create Item button.

In the filtering screen, select Query for the operation. Enter the appropriate partition key value, and click Start.

The Reply table then returns matching items.

Query Table using Java

Use the query method in Java to perform data retrieval operations. It requires specifying the partition key value, with the sort key as optional.

Code a Java query by first creating a querySpec object describing parameters. Then pass the object to the query method. We use the partition key from the previous examples.

You can review the following example −

import java.util.HashMap;
import java.util.Iterator;

import com.amazonaws.services.dynamodbv2.AmazonDynamoDBClient;
import com.amazonaws.services.dynamodbv2.document.DynamoDB;
import com.amazonaws.services.dynamodbv2.document.Item;
import com.amazonaws.services.dynamodbv2.document.ItemCollection;
import com.amazonaws.services.dynamodbv2.document.QueryOutcome;
import com.amazonaws.services.dynamodbv2.document.Table;
import com.amazonaws.services.dynamodbv2.document.spec.QuerySpec;
import com.amazonaws.services.dynamodbv2.document.utils.NameMap;

public class ProductsQuery {
   public static void main(String[] args) throws Exception {
      AmazonDynamoDBClient client = new AmazonDynamoDBClient()
         .withEndpoint("http://localhost:8000");

      DynamoDB dynamoDB = new DynamoDB(client);
      Table table = dynamoDB.getTable("Products");
      HashMap<String, String> nameMap = new HashMap<String, String>();
      nameMap.put("#ID", "ID");
      HashMap<String, Object> valueMap = new HashMap<String, Object>();
      valueMap.put(":xxx", 122);
      QuerySpec querySpec = new QuerySpec()
         .withKeyConditionExpression("#ID = :xxx")
         .withNameMap(new NameMap().with("#ID", "ID"))
         .withValueMap(valueMap);

      ItemCollection<QueryOutcome> items = null;
      Iterator<Item> iterator = null;
      Item item = null;
      try {
         System.out.println("Product with the ID 122");
         items = table.query(querySpec);
         iterator = items.iterator();

         while (iterator.hasNext()) {
            item = iterator.next();
            System.out.println(item.getNumber("ID") + ": "
               + item.getString("Nomenclature"));
         }
      } catch (Exception e) {
         System.err.println("Cannot find products with the ID number 122");
         System.err.println(e.getMessage());
      }
   }
}

Note that the query uses the partition key, however, secondary indexes provide another option for queries. Their flexibility allows querying of non-key attributes, a topic which will be discussed later in this tutorial.

The scan method also supports retrieval operations by gathering all the table data. The optional .withFilterExpression prevents items outside of specified criteria from appearing in results.

Later in this tutorial, we will discuss scanning in detail. Now, take a look at the following example −

import java.util.Iterator;

import com.amazonaws.services.dynamodbv2.AmazonDynamoDBClient;
import com.amazonaws.services.dynamodbv2.document.DynamoDB;
import com.amazonaws.services.dynamodbv2.document.Item;
import com.amazonaws.services.dynamodbv2.document.ItemCollection;
import com.amazonaws.services.dynamodbv2.document.ScanOutcome;
import com.amazonaws.services.dynamodbv2.document.Table;
import com.amazonaws.services.dynamodbv2.document.spec.ScanSpec;
import com.amazonaws.services.dynamodbv2.document.utils.NameMap;
import com.amazonaws.services.dynamodbv2.document.utils.ValueMap;

public class ProductsScan {
   public static void main(String[] args) throws Exception {
      AmazonDynamoDBClient client = new AmazonDynamoDBClient()
         .withEndpoint("http://localhost:8000");

      DynamoDB dynamoDB = new DynamoDB(client);
      Table table = dynamoDB.getTable("Products");
      ScanSpec scanSpec = new ScanSpec()
         .withProjectionExpression("#ID, Nomenclature , stat.sales")
         .withFilterExpression("#ID between :start_id and :end_id")
         .withNameMap(new NameMap().with("#ID",  "ID"))
         .withValueMap(new ValueMap().withNumber(":start_id", 120)
         .withNumber(":end_id", 129));

      try {
         ItemCollection<ScanOutcome> items = table.scan(scanSpec);
         Iterator<Item> iter = items.iterator();

         while (iter.hasNext()) {
            Item item = iter.next();
            System.out.println(item.toString());
         }
      } catch (Exception e) {
         System.err.println("Cannot perform a table scan:");
         System.err.println(e.getMessage());
      }
   }
}

Loading a table generally consists of creating a source file, ensuring the source file conforms to a syntax compatible with DynamoDB, sending the source file to the destination, and then confirming a successful population.

Utilize the GUI console, Java, or another option to perform the task.

Load Table using GUI Console

Load data using a combination of the command line and console. You can load data in multiple ways, some of which are as follows −

• The Console
• The Command Line
• Code and also
• Data Pipeline (a feature discussed later in the tutorial)

However, for speed, this example uses both the shell and console. First, load the source data into the destination with the following syntax −

aws dynamodb batch-write-item -–request-items file://[filename]

For example −

aws dynamodb batch-write-item -–request-items file://MyProductData.json

Verify the success of the operation by accessing the console at −

Choose Tables from the navigation pane, and select the destination table from the table list.

Select the Items tab to examine the data you used to populate the table. Select Cancel to return to the table list.

Load Table using Java

Employ Java by first creating a source file. Our source file uses JSON format. Each product has two primary key attributes (ID and Nomenclature) and a JSON map (Stat) −

[
   {
      "ID" : ... ,
      "Nomenclature" : ... ,
      "Stat" : { ... }
   },
   {
      "ID" : ... ,
      "Nomenclature" : ... ,
      "Stat" : { ... }
   },
    ...
]

You can review the following example −

{
   "ID" : 122,
   "Nomenclature" : "Particle Blaster 5000",
   "Stat" : {
      "Manufacturer" : "XYZ Inc.",
      "sales" : "1M+",
      "quantity" : 500,
      "img_src" : "http://www.xyz.com/manuals/particleblaster5000.jpg",
      "description" : "A laser cutter used in plastic manufacturing."
   }
}

The next step is to place the file in the directory used by your application.

Java primarily uses the putItem and path methods to perform the load.

You can review the following code example for processing a file and loading it −

import java.io.File;
import java.util.Iterator;

import com.amazonaws.services.dynamodbv2.AmazonDynamoDBClient;
import com.amazonaws.services.dynamodbv2.document.DynamoDB;
import com.amazonaws.services.dynamodbv2.document.Item;
import com.amazonaws.services.dynamodbv2.document.Table;

import com.fasterxml.jackson.core.JsonFactory;
import com.fasterxml.jackson.core.JsonParser;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper
import com.fasterxml.jackson.databind.node.ObjectNode;

public class ProductsLoadData {
   public static void main(String[] args) throws Exception {
      AmazonDynamoDBClient client = new AmazonDynamoDBClient()
         .withEndpoint("http://localhost:8000");

      DynamoDB dynamoDB = new DynamoDB(client);
      Table table = dynamoDB.getTable("Products");
      JsonParser parser = new JsonFactory()
         .createParser(new File("productinfo.json"));

      JsonNode rootNode = new ObjectMapper().readTree(parser);
      Iterator<JsonNode> iter = rootNode.iterator();
      ObjectNode currentNode;

      while (iter.hasNext()) {
         currentNode = (ObjectNode) iter.next();
         int ID = currentNode.path("ID").asInt();
         String Nomenclature = currentNode.path("Nomenclature").asText();

         try {
            table.putItem(new Item()
               .withPrimaryKey("ID", ID, "Nomenclature", Nomenclature)
               .withJSON("Stat", currentNode.path("Stat").toString()));
            System.out.println("Successful load: " + ID + " " + Nomenclature);
         } catch (Exception e) {
            System.err.println("Cannot add product: " + ID + " " + Nomenclature);
            System.err.println(e.getMessage());
            break;
         }
      }
      parser.close();
   }
}

Before using DynamoDB, you must familiarize yourself with its basic components and ecosystem. In the DynamoDB ecosystem, you work with tables, attributes, and items. A table holds sets of items, and items hold sets of attributes. An attribute is a fundamental element of data requiring no further decomposition, i.e., a field.

Primary Key

The Primary Keys serve as the means of unique identification for table items, and secondary indexes provide query flexibility. DynamoDB streams record events by modifying the table data.

The Table Creation requires not only setting a name, but also the primary key; which identifies table items. No two items share a key. DynamoDB uses two types of primary keys −

• Partition Key − This simple primary key consists of a single attribute referred to as the “partition key.” Internally, DynamoDB uses the key value as input for a hash function to determine storage.

• Partition Key and Sort Key − This key, known as the “Composite Primary Key”, consists of two attributes.

  • The partition key and

  • The sort key.

  DynamoDB applies the first attribute to a hash function, and stores items with the same partition key together; with their order determined by the sort key. Items can share partition keys, but not sort keys.

The Primary Key attributes only allow scalar (single) values; and string, number, or binary data types. The non-key attributes do not have these constraints.

Secondary Indexes

These indexes allow you to query table data with an alternate key. Though DynamoDB does not force their use, they optimize querying.

DynamoDB uses two types of secondary indexes −

• Global Secondary Index − This index possesses partition and sort keys, which can differ from table keys.

• Local Secondary Index − This index possesses a partition key identical to the table, however, its sort key differs.

API

The API operations offered by DynamoDB include those of the control plane, data plane (e.g., creation, reading, updating, and deleting), and streams. In control plane operations, you create and manage tables with the following tools −

• CreateTable
• DescribeTable
• ListTables
• UpdateTable
• DeleteTable

In the data plane, you perform CRUD operations with the following tools −

The stream operations control table streams. You can review the following stream tools −

• ListStreams
• DescribeStream
• GetShardIterator
• GetRecords

Provisioned Throughput

In table creation, you specify provisioned throughput, which reserves resources for reads and writes. You use capacity units to measure and set throughput.

When applications exceed the set throughput, requests fail. The DynamoDB GUI console allows monitoring of set and used throughput for better and dynamic provisioning.

Read Consistency

DynamoDB uses eventually consistent and strongly consistent reads to support dynamic application needs. Eventually consistent reads do not always deliver current data.

The strongly consistent reads always deliver current data (with the exception of equipment failure or network problems). Eventually consistent reads serve as the default setting, requiring a setting of true in the ConsistentRead parameter to change it.

Partitions

DynamoDB uses partitions for data storage. These storage allocations for tables have SSD backing and automatically replicate across zones. DynamoDB manages all the partition tasks, requiring no user involvement.

In table creation, the table enters the CREATING state, which allocates partitions. When it reaches ACTIVE state, you can perform operations. The system alters partitions when its capacity reaches maximum or when you change throughput.