CO3-Session-6(AmazonDynamoDB).ppt
- 1. Relational database service –DynamoDB Amazon DynamoDB is a fully managed NoSQL database service that provides fast and low-latency performance that scales with ease. Developers can create a table in Amazon DynamoDB and write an unlimited number of items with consistent latency. Amazon DynamoDB can provide consistent performance levels by automatically distributing the data and traffic for a table over multiple partitions. To help maintain consistent, fast performance levels, all table data is stored on high-performance SSD disk drives. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 2. Data Model The basic components of the Amazon DynamoDB data model include tables, items, and attributes. Individual items in an Amazon DynamoDB table can have any number of attributes, although there is a limit of 400KB on the item size. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 3. Data Types Amazon DynamoDB supports a wide range of data types for attributes. Data types fall into three major categories: Scalar, Set, or Document. Scalar Data Types A scalar type represents exactly one value. Amazon DynamoDB supports the following five scalar types: String Text and variable length characters up to 400KB. Supports Unicode with UTF8 encoding Number Positive or negative number with up to 38 digits of precision Binary Binary data, images, compressed objects up to 400KB in size Boolean Binary flag representing a true or false value Null Represents a blank, empty, or unknown state. Note:String, Number, Binary, Boolean cannot be empty. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 4. Set Data Types Sets are useful to represent a unique list of one or more scalar values. Each value in a set needs to be unique and must be the same data type. Sets do not guarantee order. Amazon DynamoDB supports three set types: String Set, Number Set, and Binary Set. String Set Unique list of String attributes Number Set Unique list of Number attributes Binary Set Unique list of Binary attributes Document Data Types Document type is useful to represent multiple nested attributes, similar to the structure of a JSON file. Amazon DynamoDB supports two document types: List and Map. Multiple Lists and Maps can be combined and nested to create complex structures. List Each List can be used to store an ordered list of attributes of different data types. Map Each Map can be used to store an unordered list of key/value pairs. Maps can be used to represent the structure of any JSON object. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 5. Primary Key Amazon DynamoDB supports two types of primary keys, and this configuration cannot be changed after a table has been created: Partition Key The primary key is made of one attribute, a partition (or hash) key. Amazon DynamoDB builds an unordered hash index on this primary key attribute. Partition and Sort Key The primary key is made of two attributes. The first attribute is the partition key and the second one is the sort (or range) key. Each item in the table is uniquely identified by the combination of its partition and sort key values. It is possible for two items to have the same partition key value, but those two items must have different sort key values. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 6. Provisioned Capacity When you create an Amazon DynamoDB table, you are required to provision a certain amount of read and write capacity to handle your expected workloads. Based on your configuration settings, DynamoDB will then provision the right amount of infrastructure capacity to meet your requirements with sustained, low-latency response times. Overall capacity is measured in read and write capacity units. These values can later be scaled up or down by using an UpdateTable action. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 7. Secondary Indexes A secondary index lets you query the data in the table using an alternate key, in Addition to queries against the primary key. Amazon DynamoDB supports two different kinds of indexes: Global Secondary Index The global secondary index is an index with a partition and sort key that can be different from those on the table. You can create or delete a global secondary index on a table at any time. Local Secondary Index The local secondary index is an index that has the same partition key attribute as the primary key of the table, but a different sort key. You can only create a local secondary index when you create a table. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 8. Writing and Reading Data Amazon DynamoDB also provides multiple querying options that let you search a table or an index or retrieve back a specific item or a batch of items. Writing Items Amazon DynamoDB provides three primary API actions to create, update, and delete items: PutItem, UpdateItem, and DeleteItem. Using the PutItem action, you can create a new item with one or more attributes. The UpdateItem action will find existing items based on the primary key and replace the attributes. This operation can be useful to only update a single attribute and leave the other attributes unchanged. Finally, you can remove an item from a table by using DeleteItem and specifying a specific primary key. Reading Items After an item has been created, it can be retrieved through a direct lookup by calling the GetItem action or through a search using the Query or Scan action. GetItem allows you to retrieve an item based on its primary key. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 9. Eventual Consistency When reading items from Amazon DynamoDB, the operation can be either eventually consistent or strongly consistent. Eventually Consistent Reads When you read data, the response might not reflect the results of a recently completed write operation. The response might include some stale data. Consistency across all copies of the data is usually reached within a second; if you repeat your read request after a short time, the response returns the latest data. Strongly Consistent Reads When you issue a strongly consistent read request, Amazon DynamoDB returns a response with the most up-to-date data that reflects updates by all prior related write operations to which Amazon DynamoDB returned a successful response. A strongly consistent read might be less available in the case of a network delay or outage. You can request a strongly consistent read result by specifying optional parameters in your request. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 10. Batch Operations Amazon DynamoDB also provides several operations designed for working with large Batches of items, including BatchGetItem and BatchWriteItem. Using the BatchWriteItem action, you can perform up to 25 item creates or updates with a single operation. Searching Items Amazon DynamoDB also gives you two operations, Query and Scan, that can be used to search a table or an index. A Query operation is the primary search operation you can use to find items in a table or a secondary index using only primary key attribute values. Each Query requires a partition key attribute name and a distinct value to search. You can Optionally provide a sort key value and use a comparison operator to refine the search results. Results are automatically sorted by the primary key and are limited to 1MB. In contrast to a Query, a Scan operation will read every item in a table or a secondary index. By default, a Scan operation returns all of the data attributes for every item in the table or index. Each request can return up to 1MB of data. Items can be filtered out using expressions, but this can be a resource-intensive operation. If the result set for a Query or a Scan exceeds 1MB, you can page through the results in 1MB increments. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 11. Scaling and Partitioning Amazon DynamoDB table can scale horizontally through the use of partitions to meet the storage and performance requirements of your application. Each individual partition represents a unit of compute and storage capacity. As storage or capacity requirements change, Amazon DynamoDB can split a partition to accommodate more data or higher provisioned request rates. After a partition is split, however, it cannot be merged back together. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.
- 12. Security Amazon DynamoDB gives you granular control over the access rights and permissions for users and administrators. Amazon DynamoDB integrates with the IAM service to Provide strong control over permissions using policies. You can create one or more policies that allow or deny specific operations on specific tables. You can also use conditions to restrict access to individual items or attributes. All operations must first be authenticated as a valid user or user session. Applications that need to read and write from Amazon DynamoDB need to obtain a set of temporary or permanent access control keys. Amazon DynamoDB Streams A common requirement for many applications is to keep track of recent changes and then perform some kind of processing on the changed records. Amazon DynamoDB Streams makes it easy to get a list of item modifications for the last 24-hour period. For example, you might need to calculate metrics on a rolling basis and update a dashboard, or maybe synchronize two tables or log activity and changes to an audit trail. With Amazon DynamoDB Streams, these types of applications become easier to build. © 2020 KL University – The contents of this presentation are an intellectual and copyrighted property of KL University.