In this article by Tanmay Deshpande, the author of the book DynamoDB Cookbook, we will cover the following topics:
Using a standalone cache for frequently accessed items
Using the AWS ElastiCache for frequently accessed items
Compressing large data before storing it in DynamoDB
Using AWS S3 for storing large items
Catching DynamoDB errors
Performing auto-retries on DynamoDB errors
Performing atomic transactions on DynamoDB tables
Performing asynchronous requests to DynamoDB
(For more resources related to this topic, see here.)
Introduction
We are going to talk about DynamoDB implementation best practices, which will help you improve the performance while reducing the operation cost. So let's get started.
Using a standalone cache for frequently accessed items
In this recipe, we will see how to use a standalone cache for frequently accessed items. Cache is a temporary data store, which will save the items in memory and will provide those from the memory itself instead of making a DynamoDB call. Make a note that this should be used for items, which you expect to not be changed frequently.
Getting ready
We will perform this recipe using Java libraries. So the prerequisite is that you should have performed recipes, which use the AWS SDK for Java.
How to do it…
Here, we will be using the AWS SDK for Java, so create a Maven project with the SDK dependency. Apart from the SDK, we will also be using one of the most widely used open source caches, that is, EhCache. To know about EhCache, refer to http://ehcache.org/.
Let's use a standalone cache for frequently accessed items:
To use EhCache, we need to include the following repository in pom.xml:
<repositories>
<repository>
<id>sourceforge</id>
<name>sourceforge</name>
<url>https://oss.sonatype.org/content/repositories/
sourceforge-releases/</url>
</repository>
</repositories>
We will also need to add the following dependency:
<dependency>
<groupId>net.sf.ehcache</groupId>
<artifactId>ehcache</artifactId>
<version>2.9.0</version>
</dependency>
Once the project setup is done, we will create a cachemanager class, which will be used in the following code:
public class ProductCacheManager {
// Ehcache cache manager
CacheManager cacheManager = CacheManager.getInstance();
private Cache productCache;
public Cache getProductCache() {
return productCache;
}
//Create an instance of cache using cache manager
public ProductCacheManager() {
cacheManager.addCache("productCache");
this.productCache = cacheManager.getCache("productCache");
}
public void shutdown() {
cacheManager.shutdown();
}
}
Now, we will create another class where we will write a code to get the item from DynamoDB. Here, we will first initiate the ProductCacheManager:
static ProductCacheManager cacheManager = new ProductCacheManager();
Next, we will write a method to get the item from DynamoDB. Before we fetch the data from DynamoDB, we will first check whether the item with the given key is available in cache. If it is available in cache, we will return it from cache itself. If the item is not found in cache, we will first fetch it from DynamoDB and immediately put it into cache. Once the item is cached, every time we need this item, we will get it from cache, unless the cached item is evicted:
private static Item getItem(int id, String type) {
Item product = null;
if (cacheManager.getProductCache().isKeyInCache(id + ":" + type)) {
Element prod = cacheManager.getProductCache().get(id + ":" + type);
product = (Item) prod.getObjectValue();
System.out.println("Returning from Cache");
} else {
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider());
client.setRegion(Region.getRegion(Regions.US_EAST_1));
DynamoDB dynamoDB = new DynamoDB(client);
Table table = dynamoDB.getTable("product");
product = table.getItem(new PrimaryKey("id", id, "type", type));
cacheManager.getProductCache().put(
new Element(id + ":" + type, product));
System.out.println("Making DynamoDB Call for getting the item");
}
return product;
}
Now we can use this method whenever needed. Here is how we can test it:
Item product = getItem(10, "book");
System.out.println("First call :Item: " + product);
Item product1 = getItem(10, "book");
System.out.println("Second call :Item: " + product1);
cacheManager.shutdown();
How it works…
EhCache is one of the most popular standalone caches used in the industry. Here, we are using EhCache to store frequently accessed items from the product table. Cache keeps all its data in memory. Here, we will save every item against its keys that are cached. We have the product table, which has the composite hash and range keys, so we will also store the items against the key of (Hash Key and Range Key).
Note that caching should be used for only those tables that expect lesser updates. It should only be used for the table, which holds static data. If at all anyone uses cache for not so static tables, then you will get stale data. You can also go to the next level and implement a time-based cache, which holds the data for a certain time, and after that, it clears the cache. We can also implement algorithms, such as Least Recently Used (LRU), First In First Out (FIFO), to make the cache more efficient.
Here, we will make comparatively lesser calls to DynamoDB, and ultimately, save some cost for ourselves.
Using AWS ElastiCache for frequently accessed items
In this recipe, we will do the same thing that we did in the previous recipe. The only thing we will change is that we will use a cloud hosted distributed caching solution instead of saving it on the local standalone cache.
ElastiCache is a hosted caching solution provided by Amazon Web Services. We have two options to select which caching technology you would need. One option is Memcached and another option is Redis. Depending upon your requirements, you can decide which one to use. Here are links that will help you with more information on the two options:
http://memcached.org/
http://redis.io/
Getting ready
To get started with this recipe, we will need to have an ElastiCache cluster launched. If you are not aware of how to do it, you can refer to http://aws.amazon.com/elasticache/.
How to do it…
Here, I am using the Memcached cluster. You can choose the size of the instance as you wish. We will need a Memcached client to access the cluster. Amazon has provided a compiled version of the Memcached client, which can be downloaded from https://github.com/amazonwebservices/aws-elasticache-cluster-client-memcached-for-java.
Once the JAR download is complete, you can add it to your Java Project class path:
To start with, we will need to get the configuration endpoint of the Memcached cluster that we launched. This configuration endpoint can be found on the AWS ElastiCache console itself. Here is how we can save the configuration endpoint and port:
static String configEndpoint = "my-elastic-
cache.mlvymb.cfg.usw2.cache.amazonaws.com";
static Integer clusterPort = 11211;
Similarly, we can instantiate the Memcached client:
static MemcachedClient client;
static {
try {
client = new MemcachedClient(new
InetSocketAddress(configEndpoint, clusterPort));
} catch (IOException e) {
e.printStackTrace();
}
}
Now, we can write the getItem method as we did for the previous recipe. Here, we will first check whether the item is present in cache; if not, we will fetch it from DynamoDB, and put it into cache. If the same request comes the next time, we will return it from the cache itself. While putting the item into cache, we are also going to put the expiry time of the item. We are going to set it to 3,600 seconds; that is, after 1 hour, the key entry will be deleted automatically:
private static Item getItem(int id, String type) {
Item product = null;
if (null != client.get(id + ":" + type)) {
System.out.println("Returning from Cache");
return (Item) client.get(id + ":" + type);
} else {
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider());
client.setRegion(Region.getRegion(Regions.US_EAST_1));
DynamoDB dynamoDB = new DynamoDB(client);
Table table = dynamoDB.getTable("product");
product = table.getItem(new PrimaryKey("id", id, "type",
type));
System.out.println("Making DynamoDB Call for getting
the item");
ElasticCache.client.add(id + ":" + type, 3600, product);
}
return product;
}
How it works…
A distributed cache also works in the same fashion as the local one works. A standalone cache keeps the data in memory and returns it if it finds the key. In distributed cache, we have multiple nodes; here, keys are kept in a distributed manner. The distributed nature helps you divide the keys based on the hash value of the keys. So, when any request comes, it is redirected to a specified node and the value is returned from there.
Note that ElastiCache will help you provide a faster retrieval of items at the additional cost of the ElastiCache cluster. Also note that the preceding code will work if you execute the application from the EC2 instance only. If you try to execute this on the local machine, you will get connection errors.
Compressing large data before storing it in DynamoDB
We are all aware of DynamoDB's storage limitations for the item's size. Suppose that we get into a situation where storing large attributes in an item is a must. In that case, it's always a good choice to compress these attributes, and then save them in DynamoDB. In this recipe, we are going to see how to compress large items before storing them.
Getting ready
To get started with this recipe, you should have your workstation ready with Eclipse or any other IDE of your choice.
How to do it…
There are numerous algorithms with which we can compress the large items, for example, GZIP, LZO, BZ2, and so on. Each algorithm has a trade-off between the compression time and rate. So, it's your choice whether to go with a faster algorithm or with an algorithm, which provides a higher compression rate.
Consider a scenario in our e-commerce website, where we need to save the product reviews written by various users. For this, we created a ProductReviews table, where we will save the reviewer's name, its detailed product review, and the time when the review was submitted. Here, there are chances that the product review messages can be large, and it would not be a good idea to store them as they are. So, it is important to understand how to compress these messages before storing them.
Let's see how to compress large data:
First of all, we will write a method that accepts the string input and returns the compressed byte buffer. Here, we are using the GZIP algorithm for compressions. Java has a built-in support, so we don't need to use any third-party library for this:
private static ByteBuffer compressString(String input)
throws UnsupportedEncodingException, IOException {
// Write the input as GZIP output stream using UTF-8 encoding
ByteArrayOutputStream baos = new ByteArrayOutputStream();
GZIPOutputStream os = new GZIPOutputStream(baos);
os.write(input.getBytes("UTF-8"));
os.finish();
byte[] compressedBytes = baos.toByteArray();
// Writing bytes to byte buffer
ByteBuffer buffer = ByteBuffer.allocate(compressedBytes.length);
buffer.put(compressedBytes, 0, compressedBytes.length);
buffer.position(0);
return buffer;
}
Now, we can simply use this method to store the data before saving it in DynamoDB. Here is an example of how to use this method in our code:
private static void putReviewItem() throws
UnsupportedEncodingException, IOException {
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider());
client.setRegion(Region.getRegion(Regions.US_EAST_1));
DynamoDB dynamoDB = new DynamoDB(client);
Table table = dynamoDB.getTable("ProductReviews");
Item product = new Item()
.withPrimaryKey(new PrimaryKey("id", 10))
.withString("reviewerName", "John White")
.withString("dateTime", "20-06-2015T08:09:30")
.withBinary("reviewMessage",
compressString("My Review Message"));
PutItemOutcome outcome = table.putItem(product);
System.out.println(outcome.getPutItemResult());
}
In a similar way, we can write a method that decompresses the data on retrieval from DynamoDB. Here is an example:
private static String uncompressString(ByteBuffer input) throws
IOException {
byte[] bytes = input.array();
ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
ByteArrayOutputStream baos = new ByteArrayOutputStream();
GZIPInputStream is = new GZIPInputStream(bais);
int chunkSize = 1024;
byte[] buffer = new byte[chunkSize];
int length = 0;
while ((length = is.read(buffer, 0, chunkSize)) != -1) {
baos.write(buffer, 0, length);
}
return new String(baos.toByteArray(), "UTF-8");
}
How it works…
Compressing data at client side has numerous advantages. Lesser size means lesser use of network and disk resources. Compression algorithms generally maintain a dictionary of words. While compressing, if they see the words getting repeated, then those words are replaced by their positions in the dictionary. In this way, the redundant data is eliminated and only their references are kept in the compressed string. While uncompressing the same data, the word references are replaced with the actual words, and we get our normal string back.
Various compression algorithms contain various compression techniques. Therefore, the compression algorithm you choose will depend on your need.
Using AWS S3 for storing large items
Sometimes, we might get into a situation where storing data in a compressed format might not be sufficient enough. Consider a case where we might need to store large images or binaries that might exceed the DynamoDB's storage limitation per items. In this case, we can use AWS S3 to store such items and only save the S3 location in our DynamoDB table.
AWS S3: Simple Storage Service allows us to store data in a cheaper and efficient manner. To know more about AWS S3, you can visit
http://aws.amazon.com/s3/.
Getting ready
To get started with this recipe, you should have your workstation ready with the Eclipse IDE.
How to do it…
Consider a case in our e-commerce website where we would like to store the product images along with the product data. So, we will save the images on AWS S3, and only store their locations along with the product information in the product table:
First of all, we will see how to store data in AWS S3. For this, we need to go to the AWS console, and create an S3 bucket. Here, I created a bucket called e-commerce-product-images, and inside this bucket, I created folders to store the images. For example, /phone/apple/iphone6.
Now, let's write the code to upload the images to S3:
private static void uploadFileToS3() {
String bucketName = "e-commerce-product-images";
String keyName = "phone/apple/iphone6/iphone.jpg";
String uploadFileName = "C:\tmp\iphone.jpg";
// Create an instance of S3 client
AmazonS3 s3client = new AmazonS3Client(new
ProfileCredentialsProvider());
// Start the file uploading
File file = new File(uploadFileName);
s3client.putObject(new PutObjectRequest(bucketName,
keyName, file));
}
Once the file is uploaded, you can save its path in one of the attributes of the product table, as follows:
private static void putItemWithS3Link() {
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider());
client.setRegion(Region.getRegion(Regions.US_EAST_1));
DynamoDB dynamoDB = new DynamoDB(client);
Table table = dynamoDB.getTable("productTable");
Map<String, String> features = new HashMap<String, String>();
features.put("camera", "13MP");
features.put("intMem", "16GB");
features.put("processor", "Dual-Core 1.4 GHz Cyclone
(ARM v8-based)");
Set<String> imagesSet = new HashSet<String>();
imagesSet.add("https://s3-us-west-2.amazonaws.com/
e-commerce-product-images/phone/apple/iphone6/iphone.jpg");
Item product = new Item()
.withPrimaryKey(new PrimaryKey("id", 250, "type", "phone"))
.withString("mnfr", "Apple").withNumber("stock", 15)
.withString("name", "iPhone 6").withNumber("price", 45)
.withMap("features", features)
.withStringSet("productImages", imagesSet);
PutItemOutcome outcome = table.putItem(product);
System.out.println(outcome.getPutItemResult());
}
So whenever required, we can fetch the item by its key, and fetch the actual images from S3 using the URL saved in the productImages attribute.
How it works…
AWS S3 provides storage services at very cheaper rates. It's like a flat data dumping ground where we can store any type of file. So, it's always a good option to store large datasets in S3 and only keep its URL references in DynamoDB attributes. The URL reference will be the connecting link between the DynamoDB item and the S3 file.
If your file is too large to be sent in one S3 client call, you may want to explore its multipart API, which allows you to send the file in chunks.
Catching DynamoDB errors
Till now, we discussed how to perform various operations in DynamoDB. We saw how to use AWS provided by SDK and play around with DynamoDB items and attributes. Amazon claims that AWS provides high availability and reliability, which is quite true considering the years of experience I have been using their services, but we still cannot deny the possibility where services such as DynamoDB might not perform as expected. So, it's important to make sure that we have a proper error catching mechanism to ensure that the disaster recovery system is in place. In this recipe, we are going to see how to catch such errors.
Getting ready
To get started with this recipe, you should have your workstation ready with the Eclipse IDE.
How to do it…
Catching errors in DynamoDB is quite easy. Whenever we perform any operations, we need to put them in the try block. Along with it, we need to put a couple of catch blocks in order to catch the errors.
Here, we will consider a simple operation to put an item into the DynamoDB table:
try {
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider());
client.setRegion(Region.getRegion(Regions.US_EAST_1));
DynamoDB dynamoDB = new DynamoDB(client);
Table table = dynamoDB.getTable("productTable");
Item product = new Item()
.withPrimaryKey(new PrimaryKey("id", 10, "type", "mobile"))
.withString("mnfr", "Samsung").withNumber("stock", 15)
.withBoolean("isProductionStopped", true)
.withNumber("price", 45);
PutItemOutcome outcome = table.putItem(product);
System.out.println(outcome.getPutItemResult());
} catch (AmazonServiceException ase) {
System.out.println("Error Message: " + ase.getMessage());
System.out.println("HTTP Status Code: " + ase.getStatusCode());
System.out.println("AWS Error Code: " + ase.getErrorCode());
System.out.println("Error Type: " + ase.getErrorType());
System.out.println("Request ID: " + ase.getRequestId());
} catch (AmazonClientException e) {
System.out.println("Amazon Client Exception :" + e.getMessage());
}
We should first catch AmazonServiceException, which arrives if the service you are trying to access throws any exception. AmazonClientException should be put last in order to catch any client-related exceptions.
How it works…
Amazon assigns a unique request ID for each and every request that it receives. Keeping this request ID is very important if something goes wrong, and if you would like to know what happened, then this request ID is the only source of information. We need to contact Amazon to know more about the request ID.
There are two types of errors in AWS:
Client errors: These errors normally occur when the request we submit is incorrect. The client errors are normally shown with a status code starting with 4XX. These errors normally occur when there is an authentication failure, bad requests, missing required attributes, or for exceeding the provisioned throughput. These errors normally occur when users provide invalid inputs.
Server errors: These errors occur when there is something wrong from Amazon's side and they occur at runtime. The only way to handle such errors is retries; and if it does not succeed, you should log the request ID, and then you can reach the Amazon support with that ID to know more about the details.
You can read more about DynamoDB specific errors at http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/ErrorHandling.html.
Performing auto-retries on DynamoDB errors
As mentioned in the previous recipe, we can perform auto-retries on DynamoDB requests if we get errors. In this recipe, we are going to see how to perform auto=retries.
Getting ready
To get started with this recipe, you should have your workstation ready with the Eclipse IDE.
How to do it…
Auto-retries are required if we get any errors during the first request. We can use the Amazon client configurations to set our retry strategy. By default, the DynamoDB client auto-retries a request if any error is generated three times. If we think that this is not efficient for us, then we can define this on our own, as follows:
First of all, we need to create a custom implementation of RetryCondition. It contains a method called shouldRetry, which we need to implement as per our needs. Here is a sample CustomRetryCondition class:
public class CustomRetryCondition implements RetryCondition {
public boolean shouldRetry(AmazonWebServiceRequest originalRequest,
AmazonClientException exception, int retriesAttempted) {
if (retriesAttempted < 3 && exception.isRetryable()) {
return true;
} else {
return false;
}
}
}
Similarly, we can implement CustomBackoffStrategy. The back-off strategy gives a hint on after what time the request should be retried. You can choose either a flat back-off time or an exponential back-off time:
public class CustomBackoffStrategy implements BackoffStrategy {
/** Base sleep time (milliseconds) **/
private static final int SCALE_FACTOR = 25;
/** Maximum exponential back-off time before retrying a request */
private static final int MAX_BACKOFF_IN_MILLISECONDS = 20 * 1000;
public long delayBeforeNextRetry(AmazonWebServiceRequest
originalRequest, AmazonClientException exception,
int retriesAttempted) {
if (retriesAttempted < 0)
return 0;
long delay = (1 << retriesAttempted) * SCALE_FACTOR;
delay = Math.min(delay, MAX_BACKOFF_IN_MILLISECONDS);
return delay;
}
}
Next, we need to create an instance of RetryPolicy, and set the RetryCondition and BackoffStrategy classes, which we created. Apart from this, we can also set a maximum number of retries. The last parameter is honorMaxErrorRetryInClientConfig. It means whether this retry policy should honor the maximum error retry set by ClientConfiguration.setMaxErrorRetry(int):
RetryPolicy retryPolicy = new RetryPolicy(customRetryCondition,
customBackoffStrategy, 3, false);
Now, initiate the ClientConfiguration, and set the RetryPolicy we created earlier:
ClientConfiguration clientConfiguration = new ClientConfiguration();
clientConfiguration.setRetryPolicy(retryPolicy);
Now, we need to set this client configuration when we initiate the AmazonDynamoDBClient; and once done, your retry policy with a custom back-off strategy will be in place:
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider(), clientConfiguration);
How it works…
Auto-retries are quite handy when we receive a sudden burst in DynamoDB requests. If there are more number of requests than the provisioned throughputs, then auto-retries with an exponential back-off strategy will definitely help in handling the load. So if the client gets an exception, then it will get auto retried after sometime; and if by then the load is less, then there wouldn't be any loss for your application.
The Amazon DynamoDB client internally uses HttpClient to make the calls, which is quite a popular and reliable implementation. So if you need to handle such cases, this kind of an implementation is a must.
In case of batch operations, if any failure occurs, DynamoDB does not fail the complete operation. In case of batch write operations, if a particular operation fails, then DynamoDB returns the unprocessed items, which can be retried.
Performing atomic transactions on DynamoDB tables
I hope we are all aware that operations in DynamoDB are eventually consistent. Considering this nature it obviously does not support transactions the way we do in RDBMS. A transaction is a group of operations that need to be performed in one go, and they should be handled in an atomic nature. (If one operation fails, the complete transaction should be rolled back.)
There might be use cases where you would need to perform transactions in your application. Considering this need, AWS has provided open sources, client-side transaction libraries, which helps us achieve atomic transactions in DynamoDB. In this recipe, we are going to see how to perform transactions on DynamoDB.
Getting ready
To get started with this recipe, you should have your workstation ready with the Eclipse IDE.
How to do it…
To get started, we will first need to download the source code of the library from GitHub and build the code to generate the JAR file. You can download the code from https://github.com/awslabs/dynamodb-transactions/archive/master.zip.
Next, extract the code and run the following command to generate the JAR file:
mvn clean install –DskipTests
On a successful build, you will see a JAR generated file in the target folder. Add this JAR to the project by choosing a configure build path in Eclipse:
Now, let's understand how to use transactions. For this, we need to create the DynamoDB client and help this client to create two helper tables. The first table would be the Transactions table to store the transactions, while the second table would be the TransactionImages table to keep the snapshots of the items modified in the transaction:
AmazonDynamoDBClient client = new AmazonDynamoDBClient(
new ProfileCredentialsProvider());
client.setRegion(Region.getRegion(Regions.US_EAST_1));
// Create transaction table
TransactionManager.verifyOrCreateTransactionTable(client,
"Transactions", 10, 10, (long) (10 * 60));
// Create transaction images table
TransactionManager.verifyOrCreateTransactionImagesTable(client,
"TransactionImages", 10, 10, (long) (60 * 10));
Next, we need to create a transaction manager by providing the names of the tables we created earlier:
TransactionManager txManager = new TransactionManager(client,
"Transactions", "TransactionImages");
Now, we create one transaction, and perform the operations you will need to do in one go. Consider our product table where we need to add two new products in one single transaction, and the changes will reflect only if both the operations are successful. We can perform these using transactions, as follows:
Transaction t1 = txManager.newTransaction();
Map<String, AttributeValue> product = new HashMap<String, AttributeValue>();
AttributeValue id = new AttributeValue();
id.setN("250");
product.put("id", id);
product.put("type", new AttributeValue("phone"));
product.put("name", new AttributeValue("MI4"));
t1.putItem(new PutItemRequest("productTable", product));
Map<String, AttributeValue> product1 = new HashMap<String, AttributeValue>();
id.setN("350");
product1.put("id", id);
product1.put("type", new AttributeValue("phone"));
product1.put("name", new AttributeValue("MI3"));
t1.putItem(new PutItemRequest("productTable", product1));
t1.commit();
Now, execute the code to see the results. If everything goes fine, you will see two new entries in the product table. In case of an error, none of the entries would be in the table.
How it works…
The transaction library when invoked, first writes the changes to the Transaction table, and then to the actual table. If we perform any update item operation, then it keeps the old values of that item in the TransactionImages table. It also supports multi-attribute and multi-table transactions. This way, we can use the transaction library and perform atomic writes. It also supports isolated reads. You can refer to the code and examples for more details at https://github.com/awslabs/dynamodb-transactions.
Performing asynchronous requests to DynamoDB
Till now, we have used a synchronous DynamoDB client to make requests to DynamoDB. Synchronous requests block the thread unless the operation is not performed. Due to network issues, sometimes, it can be difficult for the operation to get completed quickly. In that case, we can go for asynchronous client requests so that we submit the requests and do some other work.
Getting ready
To get started with this recipe, you should have your workstation ready with the Eclipse IDE.
How to do it…
Asynchronous client is easy to use:
First, we need to the AmazonDynamoDBAsync class:
AmazonDynamoDBAsync dynamoDBAsync = new AmazonDynamoDBAsyncClient(
new ProfileCredentialsProvider());
Next, we need to create the request to be performed in an asynchronous manner. Let's say we need to delete a certain item from our product table. Then, we can create the DeleteItemRequest, as shown in the following code snippet:
Map<String, AttributeValue> key = new HashMap<String, AttributeValue>();
AttributeValue id = new AttributeValue();
id.setN("10");
key.put("id", id);
key.put("type", new AttributeValue("phone"));
DeleteItemRequest deleteItemRequest = new DeleteItemRequest(
"productTable", key);
Next, invoke the deleteItemAsync method to delete the item. Here, we can optionally define AsyncHandler if we want to use the result of the request we had invoked. Here, I am also printing the messages with time so that we can confirm its asynchronous nature:
dynamoDBAsync.deleteItemAsync(deleteItemRequest,
new AsyncHandler<DeleteItemRequest, DeleteItemResult>() {
public void onSuccess(DeleteItemRequest request,
DeleteItemResult result) {
System.out.println("Item deleted successfully: "+
System.currentTimeMillis());
}
public void onError(Exception exception) {
System.out.println("Error deleting item in async way");
}
});
System.out.println("Delete item initiated" +
System.currentTimeMillis());
How it works
Asynchronous clients use AsyncHttpClient to invoke the DynamoDB APIs. This is a wrapper implementation on top of Java asynchronous APIs. Hence, they are quite easy to use and understand. The AsyncHandler is an optional configuration you can do in order to use the results of asynchronous calls. We can also use the Java Future object to handle the response.
Summary
We have covered various recipes on cost and performance efficient use of DynamoDB. Recipes like error handling and auto retries helps readers in make their application robust. It also highlights use of transaction library in order to implement atomic transaction on DynamoDB.
Resources for Article:
Further resources on this subject:
The EMR Architecture[article]
Amazon DynamoDB - Modelling relationships, Error handling[article]
Index, Item Sharding, and Projection in DynamoDB [article]
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