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DynamoDB Deep Dive
The document provides an extensive overview of Amazon DynamoDB, a managed NoSQL database service that offers seamless scalability, consistent performance, and a flexible data model without requiring a formal schema. It covers various aspects like data partitioning, provisioned throughput, and API interactions for creating and managing tables, as well as querying data. Additionally, it discusses pricing tiers, monitoring, and integration with other AWS services for optimal performance.
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DynamoDB Deep Dive
- 1. Deep dive onDynamoDB to create scalable app Eduardo Horai AWS Solutions Architect
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- 3. DynamoDB is amanaged NoSQL database service. Store and retrieve any amount of data. Serve any level of request traffic.
- 4. Without the operationalburden.
- 5. Consistent, predictable performance. Singledigit millisecond latency. Backed on solid-state drives.
- 6. Flexible data model. Key/attributepairs. No schema required. Easy to create. Easy to adjust.
- 7. Seamless scalability. No tablesize limits. Unlimited storage. No downtime.
- 8. Durable. Consistent, disk onlywrites. Replication across data centers and availability zones.
- 9. Focus on yourapp.
- 10. Provisioned throughput. Reserve IOPSfor reads and writes. Scale up for down at any time.
- 11. Pay per capacityunit. Priced per hour of provisioned throughput.
- 12. Write throughput. Size ofitem x writes per second >= 1KB
- 13. Consistent writes. Atomic incrementand decrement. Optimistic concurrency control: conditional writes.
- 14. Transactions. Item level transactionsonly. Puts, updates and deletes are ACID.
- 15. Strong or eventualconsistency Read throughput.
- 16. Strong or eventualconsistency Read throughput. Provisioned units = size of item x reads per second >= 4KB
- 17. Strong or eventualconsistency Read throughput. Provisioned units = size of item x reads per second 2
- 18. Strong or eventualconsistency Read throughput. Same latency expectations. Mix and match at ‘read time’.
- 19. Provisioned throughput is managedby DynamoDB.
- 20. Data is partitionedand managed by DynamoDB.
- 21. Partitioning • DynamoDB automaticallypartitions data by the hash key – Hash key spreads data & workload across partitions • Auto-Partitioning driven by: – Data set size – Provisioned Throughput • Tip: large number of unique hash keys and uniform distribution of workload across hash keys lends well to massive scale!
- 22. Indexed data storage. Tieredbandwidth pricing: aws.amazon.com/dynamodb/pricing
- 23. Reserved capacity. Up to53% for 1 year reservation. Up to 76% for 3 year reservation.
- 24. Authentication. Session based tominimize latency. Uses the Amazon Security T oken Service. Handled by AWS SDKs. Integrates with IAM.
- 25. Monitoring. CloudWatch metrics: latency, consumedread and write throughput, errors and throttling.
- 26. Libraries, mappers andmocks. ColdFusion, Django, Erlang, Java, .Net, Node.js, Perl, PHP Python, Ruby , http://j.mp/dynamodb-libs
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- 28. id = 100 date= 2012-05-16-09-00-10 total = 25.00 id = 101 date = 2012-05-15-15-00-11 total = 35.00 id = 101 date = 2012-05-16-12-00-10 total = 100.00
- 29. T able id = 100 date= 2012-05-16-09-00-10 total = 25.00 id = 101 date = 2012-05-15-15-00-11 total = 35.00 id = 101 date = 2012-05-16-12-00-10 total = 100.00
- 30. id = 100 date= 2012-05-16-09-00-10 total = 25.00 id = 101 date = 2012-05-15-15-00-11 total = 35.00 id = 101 date = 2012-05-16-12-00-10 total = 100.00 Item
- 31. date = 2012-05-16-09-00-10 total =25.00 id = 101 date = 2012-05-15-15-00-11 total = 35.00 id = 101 date = 2012-05-16-12-00-10 total = 100.00 id = 100 Attribute
- 32. Where is theschema? T ables do not require a formal schema. Items are an arbitrarily sized hash.
- 33. Indexing. Items are indexedby primary and secondary keys. Primary keys can be composite. Secondary keys are local to the table.
- 34.
- 35.
- 36. Hash key Range key ID Date Compositeprimary key T otal
- 37. Hash key Range key Secondaryrange key ID Date T otal
- 38. Programming DynamoDB. Small butperfectly formed API.
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- 42. Conditional updates. PutItem, UpdateItem,DeleteItem can take optional conditions for operation. UpdateItem performs atomic increments.
- 43. One API call,multiple items BatchGet returns multiple items by key. BatchWrite performs up to 25 put or delete operations. Throughput is measured by IO, not API calls.
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- 45. Query vs Scan Queryreturns items by key. Scan reads the whole table sequentially.
- 46. Query patterns Retrieve allitems by hash key. Range key conditions: ==, <, >, >=, <=, begins with, between. Counts. T and bottom n values. op Paged responses.
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- 48.
- 49. AmazonDynamoDBClient dynamoDB; =new AmazonDynamoDBClient( new ClasspathPropertiesFileCredentialsProvider()); dynamoDB.setRegion(Region.getRegion(Regions. SA_EAST_1));
- 50. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15
- 51. CreateTableRequest createPlayersTable = new CreateTableRequest().withTableName("Players") .withKeySchema(new KeySchemaElement().withAttributeName("user_id") .withKeyType(KeyType.HASH)) .withAttributeDefinitions(newAttributeDefinition() .withAttributeName("user_id").withAttributeType(ScalarAttributeType.S)) .withProvisionedThroughput(new ProvisionedThroughput() .withReadCapacityUnits(10L) .withWriteCapacityUnits(10L)); dynamoDB.createTable(createPlayersTable);
- 52. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 user_id = mza game = angry-birds score = 11,000 user_id = mza game = tetris score = 1,223,000 user_id = werner game = bejewelled score = 55,000 Scores
- 53. CreateTableRequest createScoresTable = new CreateTableRequest().withTableName(”Scores") .withKeySchema(new KeySchemaElement().withAttributeName("user_id") .withKeyType(KeyType.HASH)) .withAttributeDefinitions(newAttributeDefinition() .withAttributeName("user_id").withAttributeType(ScalarAttributeType.S)) .withKeySchema(new KeySchemaElement().withAttributeName(”game") .withKeyType(KeyType.RANGE)) .withAttributeDefinitions(newAttributeDefinition() .withAttributeName(”game").withAttributeType(ScalarAttributeType.S)) .withProvisionedThroughput(new ProvisionedThroughput() .withReadCapacityUnits(100L) .withWriteCapacityUnits(100L));
- 54. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 55. CreateTableRequest createLeaderBoardsTable = new CreateTableRequest().withTableName(”LeaderBoards") .withKeySchema(new KeySchemaElement().withAttributeName(”game") .withKeyType(KeyType.HASH)) .withAttributeDefinitions(newAttributeDefinition() .withAttributeName(”game").withAttributeType(ScalarAttributeType.S)) .withKeySchema(new KeySchemaElement().withAttributeName(”score") .withKeyType(KeyType.RANGE)) .withAttributeDefinitions(newAttributeDefinition() .withAttributeName(”score").withAttributeType(ScalarAttributeType.N)) .withProvisionedThroughput(new ProvisionedThroughput() .withReadCapacityUnits(50L) .withWriteCapacityUnits(50L));
- 56. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 Query for user Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 57. Map<String, Condition> keyConditions= new HashMap<String, Condition>(); keyConditions.put("user_id", new Condition() .withComparisonOperator(ComparisonOperator.EQ.toString()) .withAttributeValueList(new AttributeValue().withS("mza"))); QueryRequest queryRequest = new QueryRequest() .withTableName("Players") .withKeyConditions(keyConditions); QueryResult result = dynamoDB.query(queryRequest); for (Map<String, AttributeValue> item : result.getItems()) { printItem(item); }
- 58. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 Query for scores by user Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 59. Map<String, Condition> keyConditions= new HashMap<String, Condition>(); keyConditions.put("user_id", new Condition() .withComparisonOperator(ComparisonOperator.EQ.toString()) .withAttributeValueList(new AttributeValue().withS("mza"))); QueryRequest queryRequest = new QueryRequest() .withTableName(”Scores") .withAttributesToGet(”score”, “game”) .withKeyConditions(keyConditions); QueryResult result = dynamoDB.query(queryRequest); for (Map<String, AttributeValue> item : result.getItems()) { printItem(item); }
- 60. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 Query for scores by user, game Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 61. Map<String, Condition> keyConditions= new HashMap<String, Condition>(); keyConditions.put("user_id", new Condition() .withComparisonOperator(ComparisonOperator.EQ.toString()) .withAttributeValueList(new AttributeValue().withS("mza"))); keyConditions.put(”game", new Condition() .withComparisonOperator(ComparisonOperator.EQ.toString()) .withAttributeValueList(new AttributeValue().withS(”tetris"))); QueryRequest queryRequest = new QueryRequest() .withTableName(”Scores") .withKeyConditions(keyConditions); QueryResult result = dynamoDB.query(queryRequest); for (Map<String, AttributeValue> item : result.getItems()) { printItem(item); }
- 62. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 High scores by game Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 63. Map<String, Condition> keyConditions= new HashMap<String, Condition>(); keyConditions.put(”game", new Condition() .withComparisonOperator(ComparisonOperator.EQ.toString()) .withAttributeValueList(new AttributeValue().withS(”tetris"))); QueryRequest queryRequest = new QueryRequest() .withTableName(”LeaderBoards") .withKeyConditions(keyConditions) . withScanIndexForward(false); QueryResult result = dynamoDB.query(queryRequest); for (Map<String, AttributeValue> item : result.getItems()) { printItem(item); }
- 64. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 Insert Players Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 65. Map<String, AttributeValue> itemPlayer= new HashMap<String, AttributeValue>(); itemPlayer.put("user_id", new AttributeValue("eduardohorai")); itemPlayer.put("location", new AttributeValue("Sao Paulo")); itemPlayer.put("joined", new AttributeValue("27/01/2013")); PutItemRequest putItemRequest = new PutItemRequest("Players", itemPlayer); PutItemResult putItemResult = dynamoDB.putItem(putItemRequest);
- 66. Players user_id = mza location = Cambridge joined= 2011-07-04 user_id = jeffbarr location = Seattle joined = 2012-01-20 user_id = werner location = Worldwide joined = 2011-05-15 Increase writes/reads on Scores!!!!! Leader boards Scores user_id = mza game = angry-birds score = 11,000 game = angry-birds score = 11,000 user_id = mza user_id = mza game = tetris score = 1,223,000 game = tetris score = 1,223,000 user_id = mza user_id = werner game = bejewelled score = 55,000 game = tetris score = 9,000,000 user_id = jeffbarr
- 67. UpdateTableRequest updateTableRequest =new UpdateTableRequest() .withTableName("Scores") .withProvisionedThroughput(new ProvisionedThroughput() .withReadCapacityUnits(200L) .withWriteCapacityUnits(200L)); UpdateTableResult result = dynamoDB.updateTable(updateTableRequest);
- 68.
- 69. Links § aws.amazon.com/dynamodb § aws.typepad.com/brasil/ § aws.typepad.com § awshub.com.br § ehorai@amazon.com
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