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Adding Search to Relational Databases

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Amazon Web Services
Amazon Web Services

SQL is a powerful tool to query data, but it doesn't cover everything you might need. Sometimes, the precision of SQL is a limitation, that can be overcome by using the flexibility and inherent ranking of search. Learn how to use AWS servcies to create fully managed solutions using Amazon Aurora and Amazon Elasticsearch Service to combine the power of query and search.

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Darin Briskman AWS Technical Evangelist briskman@amazon.com Adding Search to Relational Databases
AWS Data Services to Accelerate Your Move to the Cloud RDS Open Source RDS Commercial Aurora Migration for DB Freedom DynamoDB & DAX ElastiCache EMR Amazon Redshift Redshift Spectrum AthenaElasticsearch Service QuickSightGlue Databases to Elevate your Apps Relational Non-Relational & In-Memory Analytics to Engage your Data Inline Data Warehousing Reporting Data Lake Amazon AI to Drive the Future Lex Polly Rekognition Machine Learning Deep Learning, MXNet Database Migration Schema Conversion
AWS Data Services to Accelerate Your Move to the Cloud RDS Open Source RDS Commercial Aurora Migration for DB Freedom DynamoDB & DAX ElastiCache EMR Amazon Redshift Redshift Spectrum AthenaElasticsearch Service QuickSightGlue Lex Polly Rekognition Machine Learning Databases to Elevate your Apps Relational Non-Relational & In-Memory Analytics to Engage your Data Inline Data Warehousing Reporting Data Lake Amazon AI to Drive the Future Deep Learning, MXNet Database Migration Schema Conversion
Multi-engine support – Open Source – Commercial – Amazon Aurora Automated provisioning, patching, scaling, backup/restore, failover Use with General Purpose SSD or Provisioned IOPS SSD storage High availability with RDS Multi-AZ Amazon RDS: Cheaper, Easier, Better
High Availability Multi-AZ Deployments Enterprise-grade fault tolerant solution for production databases Automatic failover Synchronous replication Inexpensive & enabled with one click
Up To 5x Performance Of High-end MySQL Highly Available and Durable MySQL and PostgreSQL Compatible 1/10th The Cost Of Commercial Grade Databases Fastest Growing AWS Service, Ever Amazon Aurora Speed and Availability of Commercial, Cost-Effectiveness of Open Source
BINLOG DATA DOUBLE-WRITELOG FRM FILES TYPE OF WRITE MySQL with Replica Storage MirrorStorage Mirror DC 1 DC 2 StorageStorage Primary Instance Replica Instance AZ 1 AZ 3 Primary Instance Amazon S3 AZ 2 Replica Instance ASYNC 4/6 QUORUM DISTRIBUTED WRITES Replica Instance Amazon Aurora 780K transactions 7,388K I/Os per million txns (excludes mirroring, standby) Average 7.4 I/Os per transaction MySQL IO profile for 30 min. Sysbench run 27,378K transactions 35X MORE 0.95 I/Os per transaction 7.7X LESS Aurora IO profile for 30 min. Sysbench run Aurora- Faster Because it is Built for AWS
Queries are Precise
Search text searchfacetingstructured searchsort by relevance
Amazon Elasticsearch Service Data Flow Amazon Route 53 Elastic Load Balancing AWS IAM Amazon CloudWatch Elasticsearch API AWS CloudTrail
Ways and means • All data eventually enters at the domain endpoint • Data can come in single documents (PUT) or batches (_bulk) • Some services have direct integration
Integration with Amazon S3
Kinesis Firehose delivery architecture with transformations S3 bucket source records data source source records Amazon Elasticsearch Service Firehose delivery stream transformed records delivery failure Data transformation function transformation failure
Integration with Amazon Lambda VPC Flow Logs CloudTrail Audit Logs S3 Access Logs ELB Access Logs CloudFront Access Logs SNS Notifications DynamoDB Streams SES Inbound Email Cognito Events Kinesis Streams CloudWatch Events & Alarms Config Rules Amazon Elasticsearch Service
Transforming data for Amazon Elasticsearch Service
Elasticsearch works with structured JSON { "name" : { "first" : "Jon", "last" : "Smith", } "age": 26, "city" : "palo alto", "years_employed" : 4, "interests" : [ "guitar", "sports" ] } • Documents contain fields – name/value pairs • Fields can nest • Value types include text, numerics, dates, and geo objects • Field values can be single or array • When you send documents to Elasticsearch they should arrive as JSON* *ES 5 can work with unstructured documents
If your data is not already in structured JSON, you must transform it, creating structured JSON that Elasticsearch "understands"
The most basic way to transform data • Run a script in Amazon EC2, Lambda, etc. that reads data from your data source, creates JSON documents, and ships to Amazon Elasticsearch Service directly
Logstash simplifies transformation • Logstash is open-source ETL over streams. Run colocated with your application or read from your source • Many input plugins and output plugins make it easy to connect to Logstash • Grok pattern matching to pull out values and re-write Application Instance
Elasticsearch 5 ingest processors When you index documents, you can specify a pipeline. The pipeline can have a series of processors that pre-process the data before indexing. Twenty processors are available, some are simple: { "append": { "field": "field1" "value": ["item2", "item3", "item4"] } } Others are more complex, like the Grok processor for regex with aliased expressions.
Firehose transformations add robust delivery S3 bucket source records data source source records Amazon Elasticsearch Service Firehose delivery stream transformed records delivery failure Data transformation function transformation failure • Inline calls to Lambda for free-form changes to the underlying data • Failed transforms tracked and delivered to S3
Firehose transformations add robust delivery intermediate Amazon S3 bucket backup S3 bucket source records data source source records Amazon Elasticsearch Service Firehose delivery stream transformed records transformed records transformation failure delivery failure • Inline calls to Lambda for free-form changes to the underlying data • Failed transforms tracked and delivered to S3
Common transformations • Rewrite to JSON format • Decorate documents with data from other sources • Rectify dates
Cluster is a collection of nodes Amazon ES cluster 1 3 3 1 Instance 1 2 1 1 2 Instance 2 3 2 2 3 Instance 3Dedicated master nodes Data nodes: queries and updates
Data pattern Amazon ES cluster logs_01.21.2017 logs_01.22.2017 logs_01.23.2017 logs_01.24.2017 logs_01.25.2017 logs_01.26.2017 logs_01.27.2017 Shard 1 Shard 2 Shard 3 host ident auth timestamp etc. Each index has multiple shards Each shard contains a set of documents Each document contains a set of fields and values One index per day
Indices and Mappings Index: product Type: cellphone documentId Fields: make (keyword), inventory (int), location (geo point) Type: reviews documentId Fields: make(keyword), review (text), rating (float), date (date) http://hostname/product/cellphone/1 http://hostname/product/reviews/1
Physical Layout Elasticsearch Cluster /product/cellphone/3 1 /product/cellphone/2 /product/cellphone/1 2 3 Instance 1 Instance 2 Instance 3 Cluster - 3 Instances - 3 Primary Shards - 1 Replica per primary 1 1 2 2 33 Index Operation on documents spreads it across Shards
Shards - Indexes are split into multiple shards - Primary shards are defined at index creation - Defaults to 5 Primaries and 1 Replica Shard - Shards allow - Horizontal scale - Distribute and parallelize the operations to increase throughput - Create replicas to provide high availability in case of failures
Shards … contd - Shard is a Lucene index - Number of Replica shards can be changed on the fly but not the primary shards - To change the number of primary shards, the index needs to be re-created - Shards are automatically balanced when cluster is re- sized
199.72.81.55 - - [01/Jul/1995:00:00:01 -0400] "GET /history/apollo/ HTTP/1.0" 200 6245 Document Fields host ident auth timestamp verb request status size Field indexes 199.72.81.55 unicomp6.unicomp.net 199.120.110.21 burger.letters.com 199.120.110.21 205.212.115.106 d104.aa.net 1, 4, 8, 12, 30, 42, 58, 100... Postings Elasticsearch creates an index for each field, containing the decomposed values of those fields
host:199.72.81.55 AND verb:GET 1, 4, 8, 12, 30, 42, 58, 100 ... Look up 199.72.81.55 GET 1, 4, 9, 50, 58, 75, 90, 103 ... AND Merge 1, 4, 58 Score 1.2, 3.7, 0.4 Sort 4, 1, 58 The index data structures support fast retrieval and merging. Scoring and sorting support best match retrieval
- Create Index called product - Get list of Indices health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open product 95SQ4TS 5 1 0 0 260b 260b $ curl –XPUT ‘http://hostname/product/’ Index and Document Command Examples $ curl ‘http://hostname/_cat/indices’ health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open product 95SQ4TS 5 1 0 0 260b 260b
Index and Document Command Examples .. - Indexing a document - Retrieving a document $ curl -XPUT ’http://hostname/product/cellphone/1' -H 'Content-Type: application/json' -d’ { ”make": ”Apple”, “inventory”: 100 }’ $ curl -XGET ’http://hostname/product/cellphone/1’ { "_index" : ”product", "_type" : ”cellphone", "_id" : "1", "_version" : 1, "found" : true, "_source" : { ”make": ”Apple”, “inventory: 100 } }
What happens at Index Operation http PUT – http://hostname/product/cellphone/1 Elasticsearch Cluster Instance 1 Instance 2 1 2 32 1 3 Instance 3 1. Indexing operation 2. Shard determined is based on hashing with document ID. 3. Current node forwards document to node holding the primary shard 4. Primary shard ensures all replica shards replay the same indexing operation 1 3 4
Mappings 1. Mappings are used to define types of documents. 2. Define various fields in a document 3. Mapping Types – 1. Core 1. Text or keyword 2. Numeric 3. Date 4. Boolean 2. Arrays and Multi-fields 1. Arrays – “tags” : [“blue”,”red”] 2. Multi-fields – Index same data with different settings 3. Pre-defined fields 1. _ttl, _size 2. _uid, _id, _type, _index 3. _all, _source
Mapping command examples curl -XPUT ’http://hostname/product' -H 'Content-Type: application/json' –d‘ { "mappings": { "cellphone": { "properties": { "make": { "type": "text" } } } } }’ Create an index called product with mapping, cellphone and field make as type text –
Mapping command examples curl -XPUT ’http://hostname/product/_mapping/reviews' -H 'Content-Type: application/json' -d’ { "properties": { ”review": { "type": "text" }, “rating”: { “type”: “int” } } }’ Add a new mapping, reviews, with fields review, as string and rating, as int, to existing index, product –
Mapping command examples curl -XPUT ’http://hostname/product/_mapping/cellphone' -H 'Content-Type: application/json' -d’ { "properties": { ”inventory": { "type": ”int" } } }’ Add a new field, inventory as integer, to existing mapping, cellphone in index product –
Text Queries match - full text query match_phrase - matching exact phrases or word proximity matches match_phrase_prefix - wildcard search on the final word multi_match - multi-field version of match common_terms - preference to uncommon words query_string - lucene query syntax, allowing AND|OR|NOT conditions curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query" : { "match" : { "title" : ”Red" } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query" : { "match_phrase" : { "title" : ”Red Dress" } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query" : { "match_phrase_prefix" : { "title" : "Red P” } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query": { "multi_match" : { "query": "Green", "fields": [ "title", "category" ] } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query": { ”common" : { ”body”: { "query": ”this is the green coat", "cutoff_frequency": 0.001 } } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query": { "query_string" : { "default_field" : "title", "query" : "green AND dress” } } }’
Search output No. of milliseconds taken by queryIndication if any shard timedout No. of shards that responded to this query Total no. of matching results Index and Mapping type for this document returned Relevancy score for these results

More Related Content

Adding Search to Relational Databases

  • 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Darin Briskman AWS Technical Evangelist briskman@amazon.com Adding Search to Relational Databases
  • 2. AWS Data Services to Accelerate Your Move to the Cloud RDS Open Source RDS Commercial Aurora Migration for DB Freedom DynamoDB & DAX ElastiCache EMR Amazon Redshift Redshift Spectrum AthenaElasticsearch Service QuickSightGlue Databases to Elevate your Apps Relational Non-Relational & In-Memory Analytics to Engage your Data Inline Data Warehousing Reporting Data Lake Amazon AI to Drive the Future Lex Polly Rekognition Machine Learning Deep Learning, MXNet Database Migration Schema Conversion
  • 3. AWS Data Services to Accelerate Your Move to the Cloud RDS Open Source RDS Commercial Aurora Migration for DB Freedom DynamoDB & DAX ElastiCache EMR Amazon Redshift Redshift Spectrum AthenaElasticsearch Service QuickSightGlue Lex Polly Rekognition Machine Learning Databases to Elevate your Apps Relational Non-Relational & In-Memory Analytics to Engage your Data Inline Data Warehousing Reporting Data Lake Amazon AI to Drive the Future Deep Learning, MXNet Database Migration Schema Conversion
  • 4. Multi-engine support – Open Source – Commercial – Amazon Aurora Automated provisioning, patching, scaling, backup/restore, failover Use with General Purpose SSD or Provisioned IOPS SSD storage High availability with RDS Multi-AZ Amazon RDS: Cheaper, Easier, Better
  • 5. High Availability Multi-AZ Deployments Enterprise-grade fault tolerant solution for production databases Automatic failover Synchronous replication Inexpensive & enabled with one click
  • 6. Up To 5x Performance Of High-end MySQL Highly Available and Durable MySQL and PostgreSQL Compatible 1/10th The Cost Of Commercial Grade Databases Fastest Growing AWS Service, Ever Amazon Aurora Speed and Availability of Commercial, Cost-Effectiveness of Open Source
  • 7. BINLOG DATA DOUBLE-WRITELOG FRM FILES TYPE OF WRITE MySQL with Replica Storage MirrorStorage Mirror DC 1 DC 2 StorageStorage Primary Instance Replica Instance AZ 1 AZ 3 Primary Instance Amazon S3 AZ 2 Replica Instance ASYNC 4/6 QUORUM DISTRIBUTED WRITES Replica Instance Amazon Aurora 780K transactions 7,388K I/Os per million txns (excludes mirroring, standby) Average 7.4 I/Os per transaction MySQL IO profile for 30 min. Sysbench run 27,378K transactions 35X MORE 0.95 I/Os per transaction 7.7X LESS Aurora IO profile for 30 min. Sysbench run Aurora- Faster Because it is Built for AWS
  • 9. Search text searchfacetingstructured searchsort by relevance
  • 10. Amazon Elasticsearch Service Data Flow Amazon Route 53 Elastic Load Balancing AWS IAM Amazon CloudWatch Elasticsearch API AWS CloudTrail
  • 11. Ways and means • All data eventually enters at the domain endpoint • Data can come in single documents (PUT) or batches (_bulk) • Some services have direct integration
  • 13. Kinesis Firehose delivery architecture with transformations S3 bucket source records data source source records Amazon Elasticsearch Service Firehose delivery stream transformed records delivery failure Data transformation function transformation failure
  • 14. Integration with Amazon Lambda VPC Flow Logs CloudTrail Audit Logs S3 Access Logs ELB Access Logs CloudFront Access Logs SNS Notifications DynamoDB Streams SES Inbound Email Cognito Events Kinesis Streams CloudWatch Events & Alarms Config Rules Amazon Elasticsearch Service
  • 15. Transforming data for Amazon Elasticsearch Service
  • 16. Elasticsearch works with structured JSON { "name" : { "first" : "Jon", "last" : "Smith", } "age": 26, "city" : "palo alto", "years_employed" : 4, "interests" : [ "guitar", "sports" ] } • Documents contain fields – name/value pairs • Fields can nest • Value types include text, numerics, dates, and geo objects • Field values can be single or array • When you send documents to Elasticsearch they should arrive as JSON* *ES 5 can work with unstructured documents
  • 17. If your data is not already in structured JSON, you must transform it, creating structured JSON that Elasticsearch "understands"
  • 18. The most basic way to transform data • Run a script in Amazon EC2, Lambda, etc. that reads data from your data source, creates JSON documents, and ships to Amazon Elasticsearch Service directly
  • 19. Logstash simplifies transformation • Logstash is open-source ETL over streams. Run colocated with your application or read from your source • Many input plugins and output plugins make it easy to connect to Logstash • Grok pattern matching to pull out values and re-write Application Instance
  • 20. Elasticsearch 5 ingest processors When you index documents, you can specify a pipeline. The pipeline can have a series of processors that pre-process the data before indexing. Twenty processors are available, some are simple: { "append": { "field": "field1" "value": ["item2", "item3", "item4"] } } Others are more complex, like the Grok processor for regex with aliased expressions.
  • 21. Firehose transformations add robust delivery S3 bucket source records data source source records Amazon Elasticsearch Service Firehose delivery stream transformed records delivery failure Data transformation function transformation failure • Inline calls to Lambda for free-form changes to the underlying data • Failed transforms tracked and delivered to S3
  • 22. Firehose transformations add robust delivery intermediate Amazon S3 bucket backup S3 bucket source records data source source records Amazon Elasticsearch Service Firehose delivery stream transformed records transformed records transformation failure delivery failure • Inline calls to Lambda for free-form changes to the underlying data • Failed transforms tracked and delivered to S3
  • 23. Common transformations • Rewrite to JSON format • Decorate documents with data from other sources • Rectify dates
  • 24. Cluster is a collection of nodes Amazon ES cluster 1 3 3 1 Instance 1 2 1 1 2 Instance 2 3 2 2 3 Instance 3Dedicated master nodes Data nodes: queries and updates
  • 25. Data pattern Amazon ES cluster logs_01.21.2017 logs_01.22.2017 logs_01.23.2017 logs_01.24.2017 logs_01.25.2017 logs_01.26.2017 logs_01.27.2017 Shard 1 Shard 2 Shard 3 host ident auth timestamp etc. Each index has multiple shards Each shard contains a set of documents Each document contains a set of fields and values One index per day
  • 26. Indices and Mappings Index: product Type: cellphone documentId Fields: make (keyword), inventory (int), location (geo point) Type: reviews documentId Fields: make(keyword), review (text), rating (float), date (date) http://hostname/product/cellphone/1 http://hostname/product/reviews/1
  • 27. Physical Layout Elasticsearch Cluster /product/cellphone/3 1 /product/cellphone/2 /product/cellphone/1 2 3 Instance 1 Instance 2 Instance 3 Cluster - 3 Instances - 3 Primary Shards - 1 Replica per primary 1 1 2 2 33 Index Operation on documents spreads it across Shards
  • 28. Shards - Indexes are split into multiple shards - Primary shards are defined at index creation - Defaults to 5 Primaries and 1 Replica Shard - Shards allow - Horizontal scale - Distribute and parallelize the operations to increase throughput - Create replicas to provide high availability in case of failures
  • 29. Shards … contd - Shard is a Lucene index - Number of Replica shards can be changed on the fly but not the primary shards - To change the number of primary shards, the index needs to be re-created - Shards are automatically balanced when cluster is re- sized
  • 30. 199.72.81.55 - - [01/Jul/1995:00:00:01 -0400] "GET /history/apollo/ HTTP/1.0" 200 6245 Document Fields host ident auth timestamp verb request status size Field indexes 199.72.81.55 unicomp6.unicomp.net 199.120.110.21 burger.letters.com 199.120.110.21 205.212.115.106 d104.aa.net 1, 4, 8, 12, 30, 42, 58, 100... Postings Elasticsearch creates an index for each field, containing the decomposed values of those fields
  • 31. host:199.72.81.55 AND verb:GET 1, 4, 8, 12, 30, 42, 58, 100 ... Look up 199.72.81.55 GET 1, 4, 9, 50, 58, 75, 90, 103 ... AND Merge 1, 4, 58 Score 1.2, 3.7, 0.4 Sort 4, 1, 58 The index data structures support fast retrieval and merging. Scoring and sorting support best match retrieval
  • 32. - Create Index called product - Get list of Indices health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open product 95SQ4TS 5 1 0 0 260b 260b $ curl –XPUT ‘http://hostname/product/’ Index and Document Command Examples $ curl ‘http://hostname/_cat/indices’ health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open product 95SQ4TS 5 1 0 0 260b 260b
  • 33. Index and Document Command Examples .. - Indexing a document - Retrieving a document $ curl -XPUT ’http://hostname/product/cellphone/1' -H 'Content-Type: application/json' -d’ { ”make": ”Apple”, “inventory”: 100 }’ $ curl -XGET ’http://hostname/product/cellphone/1’ { "_index" : ”product", "_type" : ”cellphone", "_id" : "1", "_version" : 1, "found" : true, "_source" : { ”make": ”Apple”, “inventory: 100 } }
  • 34. What happens at Index Operation http PUT – http://hostname/product/cellphone/1 Elasticsearch Cluster Instance 1 Instance 2 1 2 32 1 3 Instance 3 1. Indexing operation 2. Shard determined is based on hashing with document ID. 3. Current node forwards document to node holding the primary shard 4. Primary shard ensures all replica shards replay the same indexing operation 1 3 4
  • 35. Mappings 1. Mappings are used to define types of documents. 2. Define various fields in a document 3. Mapping Types – 1. Core 1. Text or keyword 2. Numeric 3. Date 4. Boolean 2. Arrays and Multi-fields 1. Arrays – “tags” : [“blue”,”red”] 2. Multi-fields – Index same data with different settings 3. Pre-defined fields 1. _ttl, _size 2. _uid, _id, _type, _index 3. _all, _source
  • 36. Mapping command examples curl -XPUT ’http://hostname/product' -H 'Content-Type: application/json' –d‘ { "mappings": { "cellphone": { "properties": { "make": { "type": "text" } } } } }’ Create an index called product with mapping, cellphone and field make as type text –
  • 37. Mapping command examples curl -XPUT ’http://hostname/product/_mapping/reviews' -H 'Content-Type: application/json' -d’ { "properties": { ”review": { "type": "text" }, “rating”: { “type”: “int” } } }’ Add a new mapping, reviews, with fields review, as string and rating, as int, to existing index, product –
  • 38. Mapping command examples curl -XPUT ’http://hostname/product/_mapping/cellphone' -H 'Content-Type: application/json' -d’ { "properties": { ”inventory": { "type": ”int" } } }’ Add a new field, inventory as integer, to existing mapping, cellphone in index product –
  • 39. Text Queries match - full text query match_phrase - matching exact phrases or word proximity matches match_phrase_prefix - wildcard search on the final word multi_match - multi-field version of match common_terms - preference to uncommon words query_string - lucene query syntax, allowing AND|OR|NOT conditions curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query" : { "match" : { "title" : ”Red" } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query" : { "match_phrase" : { "title" : ”Red Dress" } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query" : { "match_phrase_prefix" : { "title" : "Red P” } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query": { "multi_match" : { "query": "Green", "fields": [ "title", "category" ] } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query": { ”common" : { ”body”: { "query": ”this is the green coat", "cutoff_frequency": 0.001 } } } }’ curl -XGET ’http://hostname/products/product/_search' -H 'Content-Type: application/json' -d’ { "query": { "query_string" : { "default_field" : "title", "query" : "green AND dress” } } }’
  • 40. Search output No. of milliseconds taken by queryIndication if any shard timedout No. of shards that responded to this query Total no. of matching results Index and Mapping type for this document returned Relevancy score for these results