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Aws DynamoDb Part 2

Partitions and Performance

For a single partition:

- For a single partition key
- You can have a hard limit around 3000RCU (Read Capcity Units)
- You can have hard limit around 1000WCR
- Partition key should evenly distributed among partitions
- Accessing frequently a partition is called "hot partition" can cause performance issues.
- General number of requests you make to dynamodb too high

Partitions Best Practice

- Use high cardinality attributions - itemId, cardId, SessionId
- Use composite attributed - ComputerId+DisplayId
- Cache - can use DAX (dynamodb accelerator for reads)
- Add Random Numbers to make partition key high cardinality

Consistency Models

- DynamoDb supports both eventual and strongly consistent.
- By default eventual consistency.

- Eventual - Read response can be non latest, other write, stale, repeat get different answer, but keep repeating and will get latest answer.
- Strongly - Always up to date data, can get error 500, higher latency, not supported on global secondary index GSI, use more RCU throughput, use it when using api for GetItem then pass parameter you want ConsistentRead.


- Supports Transactions!
- All or nothing!
- ACID - Atomicity, Consistency, Isolation, Durability
- Read and write multiple items accross multiple tables
- Check pre requisite conditions before writing
- Group (Put, Update, Delete, ConditionCheck) in a single transaction
- API TransactWriteItems operation - succeed/fail
- TransactGetItems
- Cost - no additional to enable but if you have multiple reads costs.
- Like 2 phase commit - dynamo will perform 2 underlying reads or writes every time in transaction 1. prepare 2. commit.
- When we look at cloud watch we would see these two read/writes.

Scan / Query

- Scan - yeah full scan - avoid using it
- Can cause lot of RCU
- ProjectionExpression to select specific attributes (columns)
- Parallel scan for higher performance
- Set ConsistenRead on scan to get it to strong consistent

- Query based on primary key which is distinct for each row.
- like UserId
- Can use optional sort key to filter items based on sort key
- Results sorted by sort key
- ScanIndexForward for query with it can reverse results
- More efficient scan


LSI - Local Secondary Index

- Alternative sort key for use in scan and query
- 5 - Up to 5 LSI per table
- Sort key is one scala attribute
- Created only at table creation time
- Canno add remove modify LSI
- Same partition key as table - just a + different sort key
- Different view of the data
- UserId --> BirthDate , UserID --> (LSI) --> Height

GSI Global Secondary Index

- This is like a new table
- 1. Different partition key.  2. Different sort key.
- Create any time.
- Different partition key than orig table.
- Different sort key.
- UserId --> BirthDate, GSI: EmailId --> LoginTime
- Can specify which attributes to project to this "new table"
- Define RCW/WCU for this GSI
- Can effect performance and throttle main table when we have writes even if have enough WCU because need to reflect the new items.
- LSI not throttling.


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