GSI key selection strategy in DynamoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Global Secondary Index (GSI) in DynamoDB?

A GSI is an index with a partition key and optional sort key that can be different from the base table's keys. It allows querying data efficiently using alternate keys.

beginner

Why is key selection important when creating a GSI?

Choosing the right keys ensures efficient queries, balanced data distribution, and avoids hot partitions that can slow down performance.

intermediate

What should you consider about data distribution when selecting a GSI partition key?

Pick a key with many distinct values that spread requests evenly across partitions to prevent bottlenecks.

intermediate

How does the choice of a GSI sort key affect query flexibility?

A sort key lets you filter and sort data within a partition, enabling more precise and efficient queries.

advanced

What is a common mistake when selecting GSI keys?

Choosing keys with low cardinality or skewed data can cause hot partitions and reduce query performance.

What is the main purpose of a GSI in DynamoDB?

A To store backup copies of data

B To increase write throughput only

C To allow queries on attributes other than the primary key

D To encrypt data automatically

A GSI lets you query data efficiently using alternate keys different from the table's primary key.

Which characteristic is best for a GSI partition key?

A Few distinct values with uneven access

B Many distinct values with even access

C A constant value for all items

D A large text attribute

Many distinct values with even access help distribute load evenly and avoid hot partitions.

How does a GSI sort key improve queries?

A It allows filtering and sorting within a partition

B It encrypts the data

C It increases storage capacity

D It speeds up writes

A sort key lets you filter and sort data within the same partition key, making queries more flexible.

What happens if a GSI partition key has low cardinality?

A Hot partitions may form, causing slow queries

B Data is evenly distributed

C The index automatically scales

D Queries become faster

Low cardinality means few distinct values, which can cause uneven load and slow down queries.

Which is NOT a good practice when selecting GSI keys?

A Choosing keys with many distinct values

B Selecting keys that match query patterns

C Including a sort key for filtering

D Using keys with skewed data distribution

Keys with skewed data cause hot partitions and reduce performance, so they should be avoided.

Explain how to choose a good partition key for a GSI in DynamoDB.

Think about how to spread workload evenly and support your queries.

Describe the role of a sort key in a GSI and how it affects querying.

Consider how you can narrow down results inside one partition.

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