Why access patterns drive design in DynamoDB - Visual Breakdown

Concept Flow - Why access patterns drive design

Identify Access Patterns

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Design Table Keys

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Optimize Queries

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Efficient Data Retrieval

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Good Performance & Cost

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Repeat if needed

Start by knowing how you will access data, then design keys to match, so queries are fast and cost-effective.

Execution Sample

DynamoDB

Table: Orders
PK: CustomerID
SK: OrderDate
Query: Get all orders for a customer

Shows how designing keys based on access pattern (get orders by customer) helps efficient queries.

Execution Table

Step	Action	Input	Result	Notes
1	Identify access pattern	Get orders by customer	Access pattern defined	Focus on customer orders
2	Design primary key	PK=CustomerID, SK=OrderDate	Table keys set	Keys support query by customer
3	Query table	CustomerID=123	Returns all orders for customer 123	Efficient query using PK
4	Query with date range	CustomerID=123, OrderDate>2023-01-01	Returns recent orders	SK supports sorting/filtering
5	Try query by OrderDate only	OrderDate=2023-01-01	Fails or inefficient	No key supports this access pattern
6	Conclusion	Access patterns drive key design	Good performance only if keys match queries	Design for your queries

💡 Stop because queries not matching keys are inefficient or impossible

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
AccessPattern	undefined	Get orders by customer	Get orders by customer	Get orders by customer with date filter	Get orders by customer with date filter
PrimaryKey	undefined	PK=CustomerID, SK=OrderDate	PK=CustomerID, SK=OrderDate	PK=CustomerID, SK=OrderDate	PK=CustomerID, SK=OrderDate
QueryResult	none	none	All orders for customer 123	Filtered orders for customer 123	Filtered orders for customer 123

Key Moments - 2 Insights

Why can't we efficiently query orders by OrderDate alone?

Because the table's primary key is designed with CustomerID as the partition key, queries must include CustomerID to be efficient. Step 5 in the execution_table shows this query fails or is inefficient.

Why is the sort key OrderDate useful?

It allows sorting and filtering orders by date within a customer's orders, as shown in Step 4 where a date range query returns recent orders efficiently.

Visual Quiz - 3 Questions

Look at the execution_table, at which step do we set the table keys?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Check the 'Action' column for when keys are designed.

According to variable_tracker, what is the value of QueryResult after Step 3?

A All orders for customer 123

B none

C Filtered orders for customer 123

D undefined

💡 Hint

Look at the 'QueryResult' row under 'After Step 3' column.

If we wanted to query orders by OrderDate only, what would happen according to the execution_table?

A Query succeeds efficiently

B Query returns all orders

C Query fails or is inefficient

D Query returns no results

💡 Hint

See Step 5 in the execution_table for query by OrderDate only.

Concept Snapshot

Access patterns mean how you want to get data.
Design your table keys (partition and sort keys) to match these patterns.
This makes queries fast and cheap.
If keys don't match queries, queries fail or are slow.
Always start design by listing your access patterns.

Full Transcript

This visual execution shows why access patterns drive design in DynamoDB. First, you identify how you want to access your data, such as getting all orders for a customer. Then, you design your table keys to support that, for example using CustomerID as the partition key and OrderDate as the sort key. When you query by CustomerID, you get efficient results. You can also filter by OrderDate because it is the sort key. However, if you try to query by OrderDate alone, it fails or is inefficient because the keys don't support that pattern. This shows the importance of designing your table keys based on your access patterns to get good performance and cost.