GSI key selection strategy in DynamoDB

Introduction

A GSI key selection strategy helps you choose the best keys for your Global Secondary Index to find data quickly and efficiently.

When you want to search your data in a different way than your main table keys.

When you need to speed up queries that filter or sort by attributes not in the main key.

When you want to organize data to support new app features without changing the main table.

When you want to reduce the amount of data scanned during queries.

When you want to improve read performance for specific access patterns.

Syntax

DynamoDB

Create a GSI with:
  - Partition key (hash key): attribute to group items
  - Sort key (optional): attribute to sort items within a group
Example:
  CREATE GLOBAL SECONDARY INDEX index_name ON table_name (partition_key, sort_key);

The partition key decides how data is grouped in the index.

The sort key helps order data inside each group for faster queries.

Examples

Use when you want to find all items in a category quickly.

DynamoDB

Partition key only:
  PartitionKey = "Category"

Use when you want to find all orders by a user sorted by date.

DynamoDB

Partition key and sort key:
  PartitionKey = "UserId"
  SortKey = "OrderDate"

Use when each item is unique and you want fast single-item lookups.

DynamoDB

Partition key with high cardinality:
  PartitionKey = "TransactionId"

Sample Program

This creates a table Orders with a GSI named UserOrdersIndex. The GSI lets you find all orders by a user sorted by order date.

DynamoDB

CREATE TABLE Orders (
  OrderId STRING PRIMARY KEY,
  UserId STRING,
  OrderDate STRING,
  Category STRING
);

CREATE GLOBAL SECONDARY INDEX UserOrdersIndex ON Orders (
  UserId,
  OrderDate
);

Important Notes

Choose a partition key with many different values to spread data evenly.

Avoid using attributes with few unique values as partition keys to prevent hotspots.

Use a sort key to enable sorting and range queries within a partition.

Summary

A good GSI key strategy improves query speed and efficiency.

Partition key groups data; sort key orders data inside groups.

Pick keys based on how you want to search and organize your data.

Practice

1. What is the main purpose of choosing a good partition key for a Global Secondary Index (GSI) in DynamoDB?

easy

A. To evenly distribute data across partitions for faster queries

B. To store large binary objects efficiently

C. To encrypt data at rest automatically

D. To backup data to another region

Solution

1. Step 1: Understand partition key role in GSI

   The partition key determines how data is spread across storage partitions in DynamoDB.

2. Step 2: Importance of even data distribution

   Choosing a partition key that evenly distributes data avoids hot partitions and improves query speed.

3. Final Answer:

   To evenly distribute data across partitions for faster queries -> Option A

4. Quick Check:

   Partition key = data distribution [OK]

Hint: Partition key controls data spread, pick for balance [OK]

Common Mistakes:

• Thinking partition key orders data inside partitions
• Confusing partition key with sort key
• Assuming partition key encrypts data

2. Which of the following is the correct syntax to define a GSI with a partition key named Category and a sort key named Timestamp in a DynamoDB table creation JSON?

easy

A. {
"IndexName": "CategoryTimestampIndex",
"KeySchema": [
{"AttributeName": "Timestamp", "KeyType": "RANGE"},
{"AttributeName": "Category", "KeyType": "HASH"}
],
"Projection": {"ProjectionType": "ALL"}
}

B. {
"IndexName": "CategoryTimestampIndex",
"KeySchema": [
{"AttributeName": "Category", "KeyType": "HASH"},
{"AttributeName": "Timestamp", "KeyType": "RANGE"}
],
"Projection": {"ProjectionType": "ALL"}
}

C. {
"IndexName": "CategoryTimestampIndex",
"KeySchema": [
{"AttributeName": "Category", "KeyType": "RANGE"},
{"AttributeName": "Timestamp", "KeyType": "HASH"}
],
"Projection": {"ProjectionType": "ALL"}
}

D. {
"IndexName": "CategoryTimestampIndex",
"KeySchema": [
{"AttributeName": "Timestamp", "KeyType": "HASH"},
{"AttributeName": "Category", "KeyType": "RANGE"}
],
"Projection": {"ProjectionType": "ALL"}
}

Solution

1. Step 1: Identify partition and sort key roles

   Partition key uses KeyType "HASH" and sort key uses KeyType "RANGE" in DynamoDB.

2. Step 2: Match keys to correct KeyType

   Category is partition key so it must have KeyType "HASH"; Timestamp is sort key so it must have KeyType "RANGE".

3. Final Answer:

   Correct JSON with Category as HASH and Timestamp as RANGE -> Option B

4. Quick Check:

   Partition key = HASH, Sort key = RANGE [OK]

Hint: Partition key = HASH, sort key = RANGE in KeySchema [OK]

Common Mistakes:

• Swapping HASH and RANGE key types
• Using RANGE for partition key
• Incorrect JSON structure for KeySchema

3. Given a DynamoDB table with a GSI where the partition key is UserId and the sort key is OrderDate, what will be the result of this query?

Query on GSI with UserId = 'user123' and OrderDate between '2023-01-01' and '2023-01-31'

medium

A. All orders by user123 in January 2023 sorted by OrderDate

B. All orders by all users in January 2023

C. All orders by user123 regardless of date

D. Syntax error due to missing filter expression

Solution

1. Step 1: Understand query conditions on GSI keys

   Query uses partition key UserId = 'user123' and a range condition on sort key OrderDate between two dates.

2. Step 2: Result filters by UserId and date range

   Only items with UserId 'user123' and OrderDate in January 2023 are returned, sorted by OrderDate.

3. Final Answer:

   All orders by user123 in January 2023 sorted by OrderDate -> Option A

4. Quick Check:

   Partition key + sort key range = filtered query [OK]

Hint: Query filters partition key exactly, sort key by range [OK]

Common Mistakes:

• Thinking query returns all users' data
• Ignoring sort key range condition
• Assuming syntax error without filter expression

4. You created a GSI with partition key Category and sort key Price. Your queries on this GSI are slow and uneven. What is the most likely cause?

medium

A. Price is not indexed as a sort key

B. The table has no primary key

C. You forgot to project attributes

D. Category has very few distinct values causing hot partitions

Solution

1. Step 1: Analyze partition key distribution

   If Category has few distinct values, many items share the same partition key, causing hot partitions and slow queries.

2. Step 2: Understand impact on query performance

   Hot partitions limit throughput and cause uneven query speed despite having a sort key.

3. Final Answer:

   Category has very few distinct values causing hot partitions -> Option D

4. Quick Check:

   Few partition keys = hot partitions = slow queries [OK]

Hint: Few partition key values cause hot partitions [OK]

Common Mistakes:

• Assuming missing sort key causes slowness
• Ignoring partition key cardinality
• Thinking projection affects partitioning

5. You want to design a GSI to efficiently query orders by CustomerId and sort them by OrderStatus and then by OrderDate. Which key selection strategy is best?

hard

A. Partition key: CustomerId; Sort key: OrderDate only

B. Partition key: OrderStatus; Sort key: CustomerId#OrderDate concatenated string

C. Partition key: CustomerId; Sort key: OrderStatus#OrderDate concatenated string

D. Partition key: OrderDate; Sort key: CustomerId

Solution

1. Step 1: Identify query pattern requirements

   You want to query by CustomerId and sort first by OrderStatus, then by OrderDate.

2. Step 2: Design composite sort key for multi-level sorting

   Concatenate OrderStatus and OrderDate into one sort key string (e.g., 'Shipped#2023-06-01') to sort by status then date.

3. Step 3: Assign keys correctly

   Partition key should be CustomerId for grouping orders by customer; sort key is the concatenated string for sorting.

4. Final Answer:

   Partition key: CustomerId; Sort key: OrderStatus#OrderDate concatenated string -> Option C

5. Quick Check:

   Composite sort key enables multi-level sorting [OK]

Hint: Use composite sort key for multi-level sorting [OK]

Common Mistakes:

• Using OrderStatus as partition key causing uneven distribution
• Not combining sort keys for multi-level order
• Using OrderDate as partition key which breaks grouping