The two are not opposites, but complementary. NoSQL designs deliver faster data operations and can seem more intuitive, while not necessarily adhering to the ACID atomicity, consistency, isolation, and durability properties of a relational database.

Each of these was built for a specific range of uses and will therefore offer different features. Considering how much was at stake financially, anything less than complete reliability would simply not be tolerated. Software created in such a demanding environment and with the use of AWS-scale resources is bound to be epic. The result? Fantastic reliability and durability, with blazing fast service.

That makes DynamoDB a highly available, scalable, and distributed data store. Here are ten key features that helped make Amazon DynamoDB into a giant. With a managed service, users only interact with the running application itself. With their automatic partitioning model, as data volumes grow, DynamoDB spreads the data across partitions and raises throughput. This requires no intervention from the user. Scalar types are generally well understood. Multi-valued types are sets, which means that the values in this data type are unique.

For a months attribute you can choose a String Set with the names csc209 uoft all twelve months — each of which is, of course, unique. Similarly, document types are meant for representing complex data structures in the form of Lists and Maps.

See this example:. Tables are collections of Items, and Items are collections of Attributes. Attributes are basic units of information, like key-value pairs. Tables are like tables in relational databases, except that in DynamoDB, tables do not have fixed schemas associated with them. DynamoDB supports two kinds of Primary Keys:. GSIs span multiple partitions and are placed in separate tables. While creating a GSI, you need to carefully choose your hash key because that key will be used for partitioning.

Which is the right index type to use? In DynamoDB, data is partitioned automatically by its hash key. The partitioning logic depends upon two things: table size and throughput.

The partition for a table is calculated by DynamoDB. Although it is transparent to users, you should understand the logic behind this. Therefore, we will require three partitions.

DynamoDB streams are like transactional logs for a table.In this example, we'll learn about global secondary indexes. Just like the last lesson, we'll cover the basics first before diving into an example using some tables we've already created. Unlike local secondary indexes, you can add global secondary indexes to tables with either simple primary keys or composite primary keys. Further, you're not limited to creating a composite key schema for your index -- you can create a simple key schema.

Separate throughput. You provision read and write capacity units for a global secondary index separate than those units for the underlying table. This may add complexity and cost but also gives you flexibility to tailor the capacity to different workloads. Eventual consistency. When writing an Item to a table, it is asynchronously replicated to global secondary indexes. This means you may get different results when querying a table and a global secondary index at the same time.

You do not have the ability to specify strong consistency. No partition key size limits. Partition keys are limited to 10GB between the table Items and all local secondary indexes.

Global secondary indexes are not counted in these limits. Use on any table. Local secondary indexes may only be used on tables with composite primary keys. Global secondary indexes do not have this restriction -- you can use them on tables with simple or composite primary keys. Use with any key schema.

When specifying the key schema for your global secondary index, you can use either a simple or a composite key schema. The last two points can be confusing. The first refers to the primary key schema of the underlying tablewhile the second refers to the key schema of the secondary index you're creating. Like local secondary indexes, you may specify a global secondary index when you initially create a table. However, you may also add a global secondary index after a table is already created.

DynamoDB will backfill the global secondary index based on the existing data in the table. In this example, let's show how we might use a sparse index for our global secondary index. A sparse index is when not every Item contains the attribute you're indexing.

Only Items with the attribute s matching the key schema for your index will be copied into the index, so you may end up with fewer Items in the index than in the underlying table. Imagine we want to keep track of Orders that were returned by our Users. We'll store the date of the return in a ReturnDate attribute. The syntax to add a global secondary index is similar to that of adding a local secondary index. Note that I didn't need to redefine the keys of my underlying table in the --attribute-definitions section, only the new attribute that I was using for the index.I'm now AWS Obsessed.

I love Star Trek and Coconut Water. The emergence of cloud services has changed the way we build web-applications. This in turn has changed the responsibilities of a Web Developer. We used to build everything into a single web-application on a single server. This encompassed multiple responsibilities such as storage, databases, authentication, background jobs, caching, and more.

Cloud services allows us to reduce the complexity of our web-app and web-servers by pushing the responsibilities to these highly available, scalable, and durable cloud services. A Web Developer who knows how to deploy and integrate cloud services with a web-application is what we call a Cloud Engineer. You can print this out on the day of your exam to increase your chances of passing.

You would not have this resource if it wasn't for him. This turned it from 5 pages to 8 pages long! DynamoDB is suited for workloads with any amount of data that require predictable read and write performance and automatic scaling from large to small and everywhere in between. DynamoDB scales up and down to support whatever read and write capacity you specify per second in provisioned capacity mode.

Or you can set it to On-Demand mode and there is little to no capacity planning. Eventually consistent reads data is returned immediately but data can be inconsistent.

Copies of data will be generally consistent in 1 second. Strongly Consistent Reads will always read from the leader partition since it always has an up-to-date copy. Data will never be inconsistent but latency may be higher. Copies of data will be consistent with a guarantee of 1 second. A Partition is when DynamoDB slices your table up into smaller chunks of data.

AWS DynamoDB Schema Design - How to choose the right key

This speeds up reads for very large tables. When using a Sort key, records on the partition are logically grouped together in Ascending order. Your table s should be designed in such a way that your workload primary access patterns do not use Scans. Overall, scans should be needed sparingly, for example for an infrequent report. You can switch between these modes once every 24 hours. Provisioned Throughput Capacity is the maximum amount of capacity your application is allowed to read or write per second from a table or index.

You should enable Auto Scaling with Provisioned capacity mode. In this mode, you set a floor and ceiling for the capacity you wish the table to support.

dynamodb unique index

DynamoDB will automatically add and remove capacity to between these values on your behalf and throttle calls that go above the ceiling for too long. Throttling is when requests are blocked due to read or write frequency higher than set thresholds. Transactions let you query multiple tables at once and are an all-or-nothing approach all API calls must succeed. DynamoDB Global tables provide a fully managed solution for deploying multi-region, multi-master databases.

DynamoDB Streams allows you to set up a Lambda function triggered every time data is modified in a table to react to changes.DynamoDB is a fast and flexible nonrelational database service for any scale. DynamoDB takes away one of the main stumbling blocks of scaling databases: the management of database software and the provisioning of the hardware needed to run it.

You can deploy a nonrelational database in a matter of minutes. DynamoDB automatically scales throughput capacity to meet workload demands, and partitions and repartitions your data as your table size grows. Also, DynamoDB synchronously replicates data across three facilities in an AWS Region, giving you high availability and data durability. When reading data from DynamoDB, users can specify whether they want the read to be eventually consistent or strongly consistent:.

The primary key is the only required attribute for items in a table. You specify the primary key when you create a table, and it uniquely identifies each item. DynamoDB also provides flexible querying by letting you query on nonprimary key attributes using global secondary indexes and local secondary indexes.

A primary key can be either a single-attribute partition key or a composite partition-sort key. A single-attribute partition key could be, for example, UserID. Such a single attribute partition key would allow you to quickly read and write data for an item associated with a given user ID.

dynamodb unique index

DynamoDB indexes a composite partition-sort key as a partition key element and a sort key element. This multipart key maintains a hierarchy between the first and second element values.

For example, a composite partition-sort key could be a combination of UserID partition and Timestamp sort. Holding the partition key element constant, you can search across the sort key element to retrieve items. Such searching would allow you to use the Query API to, for example, retrieve all items for a single UserID across a range of time stamps.

Then, you can use the GetItemBatchGetItemor, if composite primary keys are enabled and in use in your table, the Query API to retrieve the items you added to the table. Each DynamoDB table has provisioned read-throughput and write-throughput associated with it. You are billed by the hour for that throughput capacity if you exceed the free tier.

Note that you are charged by the hour for the throughput capacity, whether or not you are sending requests to your table. Also, DynamoDB charges for data storage as well as the standard internet data transfer fees.

dynamodb unique index

Maximum throughput per DynamoDB table is practically unlimited. For information about the limits in place, see Limits in DynamoDB. If you want to request a limit increase, contact Amazon.If you've got a moment, please tell us what we did right so we can do more of it. Thanks for letting us know this page needs work.

We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. Some applications might need to perform many kinds of queries, using a variety of different attributes as query criteria.

To support these requirements, you can create one or more global secondary indexes and issue Query requests against these indexes in Amazon DynamoDB. To illustrate, consider a table named GameScores that tracks users and scores for a mobile gaming application. The following diagram shows how the items in the table would be organized. Not all of the attributes are shown. Now suppose that you wanted to write a leaderboard application to display top scores for each game.

A query that specified the key attributes UserId and GameTitle would be very efficient. However, if the application needed to retrieve data from GameScores based on GameTitle only, it would need to use a Scan operation. As more items are added to the table, scans of all the data would become slow and inefficient.

This makes it difficult to answer questions such as the following:. To speed up queries on non-key attributes, you can create a global secondary index. A global secondary index contains a selection of attributes from the base table, but they are organized by a primary key that is different from that of the table.

The index key does not need to have any of the key attributes from the table. It doesn't even need to have the same key schema as a table. For example, you could create a global secondary index named GameTitleIndexwith a partition key of GameTitle and a sort key of TopScore. The base table's primary key attributes are always projected into an index, so the UserId attribute is also present. The following diagram shows what GameTitleIndex index would look like.

The results are ordered by the sort key values, TopScore. If you set the ScanIndexForward parameter to false, the results are returned in descending order, so the highest score is returned first. Every global secondary index must have a partition key, and can have an optional sort key. The index key schema can be different from the base table schema. You could have a table with a simple primary key partition keyand create a global secondary index with a composite primary key partition key and sort key —or vice versa.

The index key attributes can consist of any top-level StringNumberor Binary attributes from the base table. Other scalar types, document types, and set types are not allowed. You can project other base table attributes into the index if you want. When you query the index, DynamoDB can retrieve these projected attributes efficiently. However, global secondary index queries cannot fetch attributes from the base table. For example, if you query GameTitleIndex as shown in the previous diagram, the query could not access any non-key attributes other than TopScore although the key attributes GameTitle and UserId would automatically be projected.

In a DynamoDB table, each key value must be unique. However, the key values in a global secondary index do not need to be unique. To illustrate, suppose that a game named Comet Quest is especially difficult, with many new users trying but failing to get a score above zero. The following is some data that could represent this.

Only the items with the specified key values appear in the response.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

DynamoDB tables with a primary key that is a composite hash-range key are unique. Does this extend to secondary indices too? Each entry is a comment a user has left on post. The purpose of the secondary index is to count how many unique users left a comment on a post on a specific day.

In the example above, while entry 1 and entry 2 in the LSI have the same range key value, the item in the table they point to is different. Every GSI item contains the table hash and range keys of the corresponding item.

NO they don't. Indexes are updated asynchronously, meaning they'll be eventually consistent, and which also means that dynamodb won't be able to enforce uniqueness at the time when you make the update call it won't check for uniqueness on the secondary indexes, as that's an async operation; if it does, it will have no way to return a failure, as the real-time call would already have finished. GetItem is expected to return one item, but there can be many corresponding to given secondary index in the absence of uniqueness constraint.

How are we doing? Please help us improve Stack Overflow. Take our short survey. Learn more. Asked 5 years, 9 months ago. Active 3 months ago. Viewed 14k times. Question: DynamoDB tables with a primary key that is a composite hash-range key are unique.

Active Oldest Votes. Vivek Halder Vivek Halder 91 3 3 bronze badges. Secondary indexes don't guarantee uniqueness. Mike Hornblade Mike Hornblade 1, 1 1 gold badge 13 13 silver badges 17 17 bronze badges. Sanjay Verma Sanjay Verma 11 11 silver badges 28 28 bronze badges.

How DynamoDB’s Pricing Works, Gets Expensive Quickly and the Best Alternatives

Sign up or log in Sign up using Google.In the previous lessonwe learned some basics about secondary indexes. In this lesson, we'll dive deeper into local secondary indexes. First we'll cover some basics about local secondary indexes, then we'll walk through an example. You can only add local secondary indexes on tables with composite primary keys. Must be specified at table creation. You cannot add a local secondary index to an existing table. It must be provided at creation.

This is different than global secondary indexes. This includes the size of the items in the base table plus the combined size of the items in all local secondary indexes. This is a tricky one and is a good reason for being judicious with your projected attributes. Consistency options. For local secondary indexes, you may choose between strong consistency and eventual consistency, just like on the underlying table.

Strong consistency will consume more read capacity but can be the right choice in some situations. Shares throughput with underlying table.

Global Secondary Indexes

All local secondary indexes use the read and write capacity units of the underlying table. Let's see a local secondary index in action. Remember that it can only be used on a table with a composite primary key, so that rules out our Users table from earlier. Let's apply it to our UserOrdersTable instead. Recall that we did an example in the filtering lesson about searching for a particular User's Orders that exceeded a given amount.

Because Amount wasn't a part of the primary key, we had to first retrieve all Orders for a User, then apply the filter to return only those beyond a certain amount. This query could be inefficient if we were paging through a large number of Orders for a User -- we might have to make multiple requests to find the small number of Orders over a given amount. Instead, we'll add a local secondary index using the Amount as a sort key.

This will enable fast, specific lookups using the Amount column. Unfortunately, local secondary indexes must be specified at time of table creation. First, we'll need to delete our table:. This is the same as the CreateTable command we initially issued for this table, with the additions of: 1 the "Amount" attribute definition, and 2 the "--local-secondary-indexes" flag.

dynamodb unique index

I'm not going to re-paste it here as it's too long. By this point, you should have 25 items in your recreated UserOrdersTable. You can check by running a quick Scan command to return the count:. Now that we have a table set up with a local secondary index, let's run a query against it. We can now convert this directly to a Query without using a filter:. Note that we've removed our --filter-expression and put the filter logic into the --key-condition-expression.

We also specified the --index-name that we want to query rather than hitting the table directly. Just like the filter example, we received only one Item back that satisfied our conditions.