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![©2015 Couchbase Inc. 84
Lightweight Reporting Example
function(key, values, rereduce) {
var response = {"warning" : 0, "error": 0, "fatal" : 0 };
for(i=0; i<values.length; i++) {
if (rereduce) {
response.warning = response.warning + values[i].warning;
response.error = response.error + values[i].error;
response.fatal = response.fatal + values[i].fatal;
} else {
if (values[i] == "warning")
response.warning++;
if (values[i] == "error" )
response.error++;
if (values[i] == "fatal" )
response.fatal++;
}
}
return response;
}](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-84-320.jpg)
![©2015 Couchbase Inc. 85
Lightweight Reporting Example
Query: ?group_level=2&startkey=[2010,7]&endkey=[2011,4]
{"rows":[
{"key":[2010,7], "value":{"warning":4,"error":2,"fatal":0}},
{"key":[2010,8], "value":{"warning":4,"error":3,"fatal":0}},
{"key":[2010,9], "value":{"warning":4,"error":6,"fatal":0}},
{"key":[2010,10],"value":{"warning":7,"error":6,"fatal":0}},
{"key":[2010,11],"value":{"warning":5,"error":8,"fatal":0}},
{"key":[2010,12],"value":{"warning":2,"error":2,"fatal":0}},
{"key":[2011,1], "value":{"warning":5,"error":1,"fatal":0}},
{"key":[2011,2], "value":{"warning":3,"error":5,"fatal":0}},
{"key":[2011,3], "value":{"warning":4,"error":4,"fatal":0}},
{"key":[2011,4], "value":{"warning":3,"error":6,"fatal":0}}
]
}](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-85-320.jpg)
![©2015 Couchbase Inc. 89
create index idx_odspilot on ods(DISTINCT ARRAY p.FILEN in p in PILOT END);
Array Indexing
§ Index only required elements or attributes in the array
§ Benefits are lot more significant for nested arrays/objects
§ Efficient on Index storage & search time
Transportation Company POC
• 422,137 documents.
• Query2: BEFORE array indexing
• Primary index scan
• 38.91 seconds.
• Query2: AFTER array indexing
• Array index scan [DistinctScan]
• 8.51 millisecond
• Improvement of 4572Times](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-89-320.jpg)
![©2015 Couchbase Inc. 90
Step by Step – Building indexes
§ Building and Index
1. Issue “CREATE INDEX …”
2. Place on the “next node” if WITH {“nodes”:[]}not specified
– “next node” picked by round robin
3. Create metadata for the index on the node
– “next node” picked by round robin
4. If deferred build = true
– Mark Index Status = “Pending”
– Wait for “BUILD INDEX ...”
5. Else
– Mark Index Status = “Pending”
– Start DCP Backfill with Projector
– When Done – Mark Index Status = “Online”](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-90-320.jpg)
![©2015 Couchbase Inc. 115
Document-oriented Extensions
§ Nested
– Paths—user.profile.email, children[0], children[0:2]
– NEST, UNNEST
– Ranging—EVERY child.age > 5 FOR child IN children END
– Transformations—SELECT {“name”: first_name || last_name}
§ Non-uniform
– IS MISSING
– Type checking and conversion
§ Distributed
– Direct lookup—USE KEYS
– Efficient joins—ON KEYS](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-115-320.jpg)
![©2015 Couchbase Inc. 119
Named Prepared Statement
url="http://localhost:8093/query"
s = requests.Session()
s.keep_alive = True
s.auth = ('Administrator','password')
query = {'statement':’prepare select * from `beer-sample` where type=’beer’
and name = 'Guinness’}
r = s.post(url, data=query, stream=False)
prepared = str(r.json()['results'][0]['name'])
query = {'prepared': '"' + prepared + '"' }
for i in range (0, 5000):
r = s.post(url, data=query, stream=False)
print i, r.json()['metrics']['executionTime']](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-119-320.jpg)
![©2015 Couchbase Inc. 120
Named Prepared Statement
url="http://localhost:8093/query"
s = requests.Session()
s.keep_alive = True
s.auth = ('Administrator','password')
query = {'statement':'prepare select * from `beer-sample` where name = [$1]’}
r = s.post(url, data=query, stream=False)
prepared = str(r.json()['results'][0]['name'])
for i in range (0, 5):
query={'prepared': '"' + prepared + '"', 'args': '["old_hat_brewery"]' }
r = s.post(url, data=query, stream=False)
print i, r.json()['metrics']['executionTime']
Prepare Once
Bind Values Many Times](https://image.slidesharecdn.com/couchbaseday-161206171848/85/Couchbase-Day-120-320.jpg)
More Related Content
Couchbase Day
- 1. Wi-Fi: Network –Matrix | Password - 11223344
- 2. ©2015 Couchbase Inc.2 Today’s Agenda Registration and Continental Breakfast § Labs and Environment Setup Introduction to NoSQL § Why NoSQL? § Introduction to Couchbase Couchbase Architecture § Single Node Operations § Cluster Operations Break HA and DR § Availability and XDCR § Backup/Restore § Security 12:00 – Lunch Break Developing with Couchbase § Couchbase SDKs Couchbase Indexing § Couchbase GSI andViews § Views Overview Break Indexing and Query § Couchbase Indexes (GSI/MOI) § Couchbase Query (N1QL) Couchbase Mobile § Overview Adjourn § Close
- 3.
- 4. ©2015 Couchbase Inc.4 Why companies need a new database solution § What are the biggest problems with Relational Databases that are driving adoption of NoSQL? LACKOFFLEXIBILITY/ HASRIGIDSCHEMAS INABILITY TOSCALEOUT PERFORMANCE CHALLENGES 49% 69% 50% 47% 44% COST ALLOFTHEABOVE 35% 29% 16% 12%
- 5. ©2015 Couchbase Inc.5 Dynamo October 2007 Cassandra August 2008 Bigtable November 2006 Voldemort February 2009 Very few organizations want to (fewer can) build and maintain database software technology. But every organization participating in the digital economy needs this technology. Why NoSQL? New data solutions to address emerging needs: § Performance and Scalability § High Availability with Data Synchronization (multi-datacenter) § Flexible Schema to accommodate frequent changes § Transparent/Auto-Sharding without application participation § Distributed System for Querying § Managed Cache
- 6. ©2015 Couchbase Inc.6Scaling out flattens the cost and performance curves NoSQL Database Scales Out Cost and performance mirrors app tier Users NoSQL Distributed Data Store Web/App Server Tier Application Scales Out Just add more commodity web servers Users System Cost Application Performance Application Performance System Cost Why NoSQL
- 7. ©2015 Couchbase Inc.7 How has Couchbase Server evolved beyond NoSQL? § Discreet, scalable services (data, index, query) § Filtering for cross data center replication § SQL-based query language and secondary indexing § Geospatial search and indexing § CRUD, prepared statements, and covering indexes § Memory-optimized Indexes § Array Indexing within Nested JSON § Graphical QueryW0rkbench § Sub-Document API § LDAP integration and auditing; RBAC, X-509 Certificates
- 8. ©2015 Couchbase Inc.8 Next Generation Data Management Architecture Data Sources Enterprise Applications Social Media Web & Ecommerce Mobile AppsSensor Data Mainframe External Data System Logs Data Integration Speed Layer Batch Layer Data Management Data Lake Data Warehouse Business Intelligence Dashboards Query ToolsData Access In Memory Cache Data Serving Layer
- 9. ©2015 Couchbase Inc.9 Next Generation Data Management Architecture Enterprise Applications Social Media Web & Ecommerce Mobile AppsSensor Data MainframeExternal DataSystem Logs Speed Layer Batch Layer Data Lake Business Intelligence Dashboards Query Tools In Memory Cache Data Warehouse N1QL Query Workbench Data Sources Data Integration Data Management Data Access Data Serving Layer Real Time Data Ingestion
- 10. ©2015 Couchbase Inc.10 Couchbase Ecosystem
- 11. ©2015 Couchbase Inc.11 How Does Couchbase Fit In Big Data? Couchbase and Hadoop are complementary.
- 12. ©2015 Couchbase Inc.12 Couchbase and Hadoop New Data Stream MergedView All Data Precompute Views (Map Reduce) Process Stream Incremental Views Partial Aggregate Partial Aggregate Partial Aggregate Real-Time Data Batch Recompute BatchViews Real-TimeViews Real-Time Increment Merge Batch Layer Serving Layer Speed Layer Couchbase Hadoop Connector (Sqoop)
- 13. ©2015 Couchbase Inc.13 Couchbase Hadoop Connector (Sqoop) Couchbase and Hadoop New Data Stream MergedView All Data Precompute Views (Map Reduce) Process Stream Incremental Views Partial Aggregate Partial Aggregate Partial Aggregate Real-Time Data Batch Recompute BatchViews Real-TimeViews Real-Time Increment Merge Batch Layer Serving Layer Speed Layer Stream / Data Ingestion Store Incremental Data / Stream processing Serving merged results / responses
- 14. ©2015 Couchbase Inc.14 Couchbase Server on Docker and OrchestrationTechnologies Official Docker Support with MacOS, Ubuntu is coming Couchbase & Containers
- 15. ©2015 Couchbase Inc.15 Platform as a Service § Couchbase runs on PaaS environments Current Focus Future Plan
- 16. ©2015 Couchbase Inc.16 Infrastructure as a Service Couchbase supports IaaS environments § Available in AWS marketplace § BYOL or hourly pricing § Available in Azure marketplace § BYOL or hourly pricing § Marketplace image in the works § BYOL at the moment § Hourly pricing soon Other providers supported
- 17. Couchbase Overview What isCouchbase?
- 18. ©2015 Couchbase Inc.18 Couchbase Server Defined § Couchbase Server 4.X is the first NoSQL Database to enable you to develop with agility and operate at any scale. Managed Cache Key-Value Store Document Database Embedded Database Sync Management
- 19. ©2015 Couchbase Inc.19 The Power of the Flexible JSON Schema Ability to store data in multiple ways • Denormalized single document, as opposed to normalizing data across multiple table • Dynamic Schema to add new values when needed
- 20. ©2015 Couchbase Inc.20 Document Fundamentals - Composition § Similar to primary keys in relational databases § Documents are partitioned based on the document ID § ID based document lookup is extremely fast § Must be unique § JSON § Binary - integers, strings, booleans § Common binary values include serialized objects, compressed XML, compressed text, encrypted values Document ID or Key Value § CASValue (unique identifier for concurrency) § TTL § Flags (optional client library metadata) § Revision # Metadata Document Document ID / Key MetadataValue
- 21. ©2015 Couchbase Inc.21 Storing And Retrieving Documents
- 22. ©2015 Couchbase Inc.22 Couchbase Data Access • Everything is layered on top of KeyValue • A Document store is a special case of Key- Value • Views provide aggregation and real-time analytics through incremental map-reduce • Global Secondary Indexes provide low latency/high throughput indexes • N1QL is a language that provides a powerful and expressive way of accessing documents
- 23.
- 24. ©2015 Couchbase Inc.24 Couchbase Architecture • Data Service – builds and maintains Distributed secondary indexes (MapReduceViews) • Indexing Engine – builds and maintains Global Secondary Indexes • Query Engine – plans, coordinates, and executes queries against either Global or Distributed indexes • Cluster Manager – configuration, heartbeat, statistics, RESTful Management interface
- 25. ©2015 Couchbase Inc.25 Couchbase Read Operation APPLICATION SERVER MANAGED CACHE DISK DISK QUEUE REPLICATION QUEUE DOC 1 GET DOC 1 DOC 1
- 26. ©2015 Couchbase Inc.26 APPLICATION SERVER MANAGED CACHE DISK DISK QUEUE REPLICATION QUEUE Write Operation DOC 1 DOC 1DOC 1
- 27. ©2015 Couchbase Inc.27 Cache Ejection APPLICATION SERVER MANAGED CACHE DISK DISK QUEUE REPLICATION QUEUE DOC 1 DOC 2DOC 3DOC 4DOC 5 DOC 1 DOC 2 DOC 3 DOC 4 DOC 5
- 28. ©2015 Couchbase Inc.28 APPLICATION SERVER MANAGED CACHE DISK DISK QUEUE REPLICATION QUEUE DOC 1 Cache Miss DOC 2 DOC 3 DOC 4 DOC 5 DOC 2 DOC 3 DOC 4 DOC 5 GET DOC 1 DOC 1 DOC 1
- 29.
- 30. ©2015 Couchbase Inc.30 Auto sharding – Bucket and vBuckets vB Data buckets vB 1 ….. 1024 Active Virtual buckets vB vB 1 ….. 1024 Replica Virtual buckets
- 31. ©2015 Couchbase Inc.31 Cluster Map Couchbase Cluster CLUSTER MAP Hashing Algorithm CRC32 vBucket2 vBucket3 vBucket4 vBucket5 vBucket6 vBucket7 vBucket1024 vBucket1 ... Couchbase SDK Couchbase Cluster CLUSTER MAP Hashing Algorithm CRC32 vBucket2 vBucket3 vBucket4 vBucket5 vBucket6 vBucket7 vBucket1024 vBucket1 ... Couchbase SDK
- 32. ©2015 Couchbase Inc.32 ACTIVE ACTIVE ACTIVE REPLICA REPLICA REPLICA Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 Basic Operation SHARD 5 SHARD 2 SHARD 9 SHARD SHARD SHARD SHARD 4 SHARD 7 SHARD 8 SHARD SHARD SHARD SHARD 1 SHARD 3 SHARD 6 SHARD SHARD SHARD SHARD 4 SHARD 1 SHARD 8 SHARD SHARD SHARD SHARD 6 SHARD 3 SHARD 2 SHARD SHARD SHARD SHARD 7 SHARD 9 SHARD 5 SHARD SHARD SHARD
- 33. ©2015 Couchbase Inc.33 Rebalance ACTIVE ACTIVE ACTIVE REPLICA REPLICA REPLICA Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 ACTIVE ACTIVE REPLICA REPLICA Couchbase Server 4 Couchbase Server 5 SHARD 5 SHARD 2 SHARD SHARD SHARD 4 SHARD SHARD SHARD 1 SHARD 3 SHARD SHARD SHARD 4 SHARD 1 SHARD 8 SHARD SHARD SHARD SHARD 6 SHARD 3 SHARD 2 SHARD SHARD SHARD SHARD 7 SHARD 9 SHARD 5 SHARD SHARD SHARD SHARD 7 SHARD SHARD 6 SHARD SHARD 8 SHARD 9 SHARD READ/WRITE/UPDATE
- 34. ©2015 Couchbase Inc.34 Fail Over ACTIVE ACTIVE ACTIVE REPLICA REPLICA REPLICA Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 ACTIVE ACTIVE REPLICA REPLICA Couchbase Server 4 Couchbase Server 5 SHARD 5 SHARD 2 SHARD SHARD SHARD 4 SHARD SHARD SHARD 1 SHARD 3 SHARD SHARD SHARD 4 SHARD 1 SHARD 8 SHARD SHARD SHARDSHARD 6 SHARD 2 SHARD SHARD SHARD SHARD 7 SHARD 9 SHARD 5 SHARD SHARD SHARD SHARD 7 SHARD SHARD 6 SHARDSHARD 8 SHARD 9 SHARD SHARD 3 SHARD 1 SHARD 3 SHARD
- 35. High Availability withCouchbase 35
- 36. ©2015 Couchbase Inc.36 Recovering from a Fail Over ACTIVE ACTIVE ACTIVE REPLICA REPLICA REPLICA Couchbase Server 1 Couchbase Server 2 Couchbase Server 3 ACTIVE ACTIVE REPLICA REPLICA Couchbase Server 4 Couchbase Server 5 SHARD 5 SHARD 2 SHARD SHARD SHARD 4 SHARD SHARD SHARD 1 SHARD 3 SHARD SHARD SHARD 4 SHARD 1 SHARD 8 SHARD SHARD SHARDSHARD 6 SHARD 2 SHARD SHARD SHARD SHARD 7 SHARD 9 SHARD 5 SHARD SHARD SHARD SHARD 7 SHARD SHARD 6 SHARDSHARD 8 SHARD 9 SHARD SHARD 3 SHARD 1 SHARD 3 SHARD
- 37. ©2015 Couchbase Inc.37 Recovering from Failover Three options: 1. Remove the node 2. Full recovery 3. Delta node recovery
- 38. ©2015 Couchbase Inc.38 Delta Node Recovery Recover Failed Nodes with incremental Catch-up § Improve recovery time for nodes with large datasets § Add a failed node into cluster and recover data from where it left off
- 39. ©2015 Couchbase Inc.39 Rack Zone Awareness – Server Groups Informs Couchbase as to the physical distribution of nodes in racks or availability groups. The Active vBuckets in Server 1 will have its replica’s in a different group. Never in the same group. Couchbase Server 3 Couchbase Server 2 Couchbase Server 6 Couchbase Server 5 Couchbase Server 4 Couchbase Server 9 Couchbase Server 8 Couchbase Server 7 Ensures that replica vBuckets are distributed across groups REPLICA REPLICA Group: A Nodes 1,2,3 Group: B Nodes 4,5,6 Group: C Nodes 7,8,9 Couchbase Server 1 ACTIVE
- 40.
- 41.
- 42. ©2015 Couchbase Inc.42 Cross Datacenter Replication (XDCR) TO OTHER NODE APPLICATION SERVER MANAGED CACHE DISK DISK QUEUE REPLICATION QUEUE XDCR QUEUE TO OTHER CLUSTER DOC 1 DOC 1DOC 1DOC 1
- 43. ©2015 Couchbase Inc.43 Cross Datacenter Replication (XDCR) TO OTHER NODE APPLICATION SERVER MANAGED CACHE DISK DISK QUEUE REPLICATION QUEUE XDCR QUEUE TO OTHER CLUSTER DOC 1 DOC 1DOC 1DOC 1 Single-node type means easier administration and scaling CLUSTER REPLICATION vBucket REPLICAS
- 44. ©2015 Couchbase Inc.44 XDCR: Cluster Topology Aware • Applications can access both Clusters (active-active) • Can provide filtered replication • One-one, one-many, many-one • Differently sized clusters • Different HW platforms • Mixed versions supported (Upgrades)
- 45. ©2015 Couchbase Inc.45 Cross Datacenter Replication (XDCR) Unidirectional or Bidirectional Replication Unidirectional § Hot spare / Disaster Recovery § Development/Testing copies § Connectors (Solr, Elasticsearch) § Integrate to custom consumer Bidirectional § Multiple Active Masters § Disaster Recovery § Datacenter Locality
- 46.
- 47. ©2015 Couchbase Inc.47 Key Features § Zero downtime backup and restore § Single utility to manage multiple clusters § Restore from any point, to any bucket or topology § Fully differential – merge backups to maintain desired restore points § Built-in concurrency control § Resume interrupted backups § Compaction reduces storage requirements
- 48. ©2015 Couchbase Inc.48 Enterprise Backup and Restore § >10x Faster Backups § Better Data Protection with Enterprise Backup Restore – Scale to larger datasets – Fast backups and restores – Better Consistency in backup data – Resilient to failures/Topology Changes 288 25 0 50 100 150 200 250 300 350 BackUpMB/Sec 4.5 Backup Speed 4.1 Backup Speed
- 49. ©2015 Couchbase Inc.49 Data Import § Create backup repo /opt/couchbase/bin/cbbackupmgr config --archive /tmp/backup --repo demo § Import of sample data /opt/couchbase/bin/cbbackupmgr json --host couchbase://172.23.107.27:8091 --username Administrator --password password --bucket travel-sample --dataset /opt/couchbase/samples/travel-sample.zip --format sample § Import of list data /opt/couchbase/bin/cbbackupmgr json --host couchbase://172.23.107.27:8091 --username Administrator --password password --bucket chicago --dataset file:///root/data/companies.json --format list --generate-key %permalink%::#MONO_INCR#
- 50.
- 51.
- 52. ©2015 Couchbase Inc.52 Security with Couchbase § Administrative Security: § Admin user (full access) vs. Read-only user (monitoring, developer access) § SSL encryption to REST API and Web UI § HTTP access log § Data Security: § Applications connect via SASL with single user/pass § Data-at-Rest encryption via partnership withVormetric § SSL encryption for over-the-wire § Couchbase 4.0: § LDAP integration § More extensive administrative action auditing More at: http://docs.couchbase.com/admin/admin/security/security-best-practices.html
- 53. ©2015 Couchbase Inc.53 Couchbase authentication overview § Application authentication § Buckets are protected with challenge-response SASL protocol § AuthN happens over CRAM-MD5 § Admin authentication § Authentication through admin username and password § Authentication through LDAP (New in 4.0) AUTHENTICATION
- 54. ©2015 Couchbase Inc.54 Couchbase authorization overview § Application data access § Full access to the bucket application is connected to § Admin access § Full administrator has full privileges on the cluster § Read-only administrator cannot change cluster settings AUTHORIZATION
- 55. ©2015 Couchbase Inc.55 Couchbase encryption overview § Data-in-motion encryption § Client-server communication can be encrypted using SSL § Secure admin access using SSL over port 18091 § Secure view access using SSL over port 18092 § Secure XDCR for encryption across datacenters Track all Access SERVER 3SERVER 1 SERVER 2 Couchbase Server – New York SERVER 3SERVER 1 SERVER 2 Couchbase Server – London SSL Client applications Secure XDCR over SSL Admin access over port 18091 SSL View access over port 18092 SSL https://couchbase_server:18091/… https://couchbase_server:18092/… ENCRYPTION
- 56. ©2015 Couchbase Inc.56 Couchbase encryption overview § Transparent data-at-rest encryption solution ENCRYPTION Storage Database Application User File Systems Volume Managers DSM Vormetric Data Security Manager on Enterprise premise or in cloud virtual or physical appliance • Centrally manage keys and policy • Virtual and physical appliance • High-availability with cluster • Multi-tenant and strong separation of duties • Proven 10,000+ device and key management scale • Web, CLI, API Interfaces • FIPS 140-2 certified Secure Personally Identifiable Information • User profile information • Login Credentials • IP Addresses
- 57. ©2015 Couchbase Inc.57 External identity management using LDAP Centralized identity management § Define multiple read-only admins and full-admins § Centralized security policy management for admin accounts for stronger passwords, password rotation, and auto lockouts Individual accountability. Simplified compliance. § Define UIDs in LDAP, and map UIDs to read-only / full admin role in Couchbase § Comprehensive audit trails with LDAP UIDs in audit records
- 58. ©2015 Couchbase Inc.58 New UI for authorizing LDAP administrators Turn on/off LDAP Add UIDs to read-only admins Add UIDs to full admins Set default behavior if UID is not mapped Testing credentials to verify what level of access Plus REST, and CLI integration for programmatic setup
- 59. ©2015 Couchbase Inc.59 Admin Auditing in Couchbase Rich audit events § Over 25 different, detailed admin audit events § Auditing for tools including backup Configurable auditing § Configurable file target § Support for time based log rotation and audit filtering Easy integration § JSON format allows for easy integration with downstream systems using flume, logstash, and syslogd
- 60. ©2015 Couchbase Inc.60 Auditing a successful login { "timestamp":"2015-02-20T08:48:49.408-08:00", "id":8192, "name":"login success", "description":"Successful login to couchbase cluster", "role":"admin", "real_userid": { "source":"ns_server", "user":"bjones” }, "sessionid":"0fd0b5305d1561ca2b10f9d795819b2e", "remote":{"ip":"172.23.107.165", "port":59383} } WHEN WHO WHAT HOW
- 61.
- 62.
- 63.
- 64. ©2015 Couchbase Inc.64 Languages and Interfaces for Couchbase § Official SDKs – Java – .NET – Node.js – Python § For each of these we have – Full Document support – Interoperability – Common Programming Model Others: Erlang, Perl,TCL, Clojure, Scala - PHP - C - Go - Ruby JDBC and ODBC
- 65. ©2015 Couchbase Inc.65 Interacting with Data Cluster Bucket CRUD View Query N1QL Query
- 66. ©2015 Couchbase Inc.66 Couchbase SDKs § What does it mean to be a Couchbase SDK? Cluster Bucket CRUD Functional Give the application developer a concurrent API for basic (k-v) or document management API get() insert() replace() upsert() remove()
- 67. ©2015 Couchbase Inc.67 The Document § Documents are integral to the SDKs. § All SDKs support JSON format § In addition: Serialized objects, Unquoted Strings, Binary pass-through § A Document contains: 67 Property Description ID The bucket-unique identifier Content The value that is stored Expiry An expiration time CAS Check-and-Set identifier
- 68. ©2015 Couchbase Inc.68 Bucket API – Modifying Documents § insert() the document if it does not exist § replace() the document if it does exist § upsert() the document (insert or replace) § remove() the document § append() data to the document § prepend() data to the document § counter() for increment/decrement type operations
- 69. ©2015 Couchbase Inc.69 Bucket API – Retrieving Documents § get() the document § getFromReplica() if the master is not available § getAndTouch() to load the document and reset the expiry § getAndLock() to load the document and write-lock it
- 70. ©2015 Couchbase Inc.70 Unified API – DML, CAS Example 1. Two Clients retrieve the same document "XYZ" 2. Client A retrieves it first. 3. Client B then retrieves XYZ. Both clients will have the same CAS value for document XYZ 4. Client B tries to perform an update to document XYZ. The update succeeds as the CAS value was unchanged from when Client B initially retrieved the document. Once the update succeeds, the CAS value for XYZ changes. 5. Client A then tries to perform an update on XYZ immediately after Client B. The update will fail as Client A's CAS value is out of date. When Client B updated XYZ, the CAS value changed. ©2014 Couchbase, Inc.
- 71. ©2015 Couchbase Inc.71 Durability Options for writes APPLICATION SERVER CACHE DISK XDCR QUEUE TO OTHER CLUSTER CACHE DISK DISK QUEUE REPLICATION QUEUE APPLICATION SERVER DISK DISK QUEUE CACHE REPLICATION QUEUE DISK QUEUE DOC DOCDOC DOCDOCDOC DOC DOC CLUSTER 1 NODE 2 CLUSTER 2 NODE … CLUSTER 1 NODE 1 DOC ( PUT ) By default, a write operation is successful when the data has been written to the memory of the node with the active vBucket. Eventually (but pretty quickly), the data will also be written to persistent storage and to replica vBuckets. ( replicateTo ) write operation is successful when data has been written to both the active vBucket and replica vBuckets. ( persistTo ) write operation is successful when data has been written to persistent storage • Can be set to require writes to persistent storage for replicas. • Using persistTo will slow performance.
- 72. ©2015 Couchbase Inc.72 Bucket API – Querying § query() is possible for § Views § N1QL § Streams N response rows as they arrive from the server § Loads more than one Document based on Criteria § Typically used to satisfy secondary and advanced querying use cases
- 73.
- 74.
- 75. ©2015 Couchbase Inc.75 Which to choose – GSI vsViews Workloads New GSI in v4.X Map/ReduceViews Complex Reporting Just InTime Aggregation Pre-aggregated Ad-hoc Querying Ad-hoc View per Query Flexible Indexing Logic N1QL Functions Javascript Secondary Lookups Faster with Single Node Lookup Slower due to Scatter-Gather Range Scans Faster with Single Node Lookup Slower due to Scatter-Gather
- 76. ©2015 Couchbase Inc.76 Comparing GSI andViews Capabilities Global Secondary Indexes Map/ReduceViews Partitioning Model Independent – Indexing Service Aligned to Data – Data Service Scale Model Independently Scale Index Scale with Data Service Secondary Lookup Single Node Scatter-Gather Range Scan Single Node Scatter-Gather Grouping, Aggregates With N1QL Built-in withViews API Caching Managed Not Managed Storage ForestDB Couchstore Availability Identical Indexes load balanced Replica Based
- 77. ©2015 Couchbase Inc.77 Indexing Differences § Local secondary indexes (Views) – Co-located with data – Higher write performance / Lower read performance: scatter-gather – Map/ReduceViews: programmable indexer for complex reporting and indexing logic. – SpatialViews: Incremental R-tree indexing for powerful bounding-box queries – Full partition alignment and paired scalability with Data Service § Global secondary indexes (GSI / MOI) – Independently partitioned and independently scalable indexes in Indexing Service – Isolated from Key-Value operations – Low latency queries without compromising on mutation performance (insert/update/delete) – Higher query performance – Async writes to a large number of global indexes – Memory Optimized Indexes (MOI) alternate storage option to standard GSI - small memory footprint, optimized for lowest latency queries
- 78. ©2015 Couchbase Inc.78 Indexing with N1QL Indexing in N1QL Global and Local Indexes IndexingTypes for N1QL View Based Indexes • Local Indexes: • PartitionedAligned to Data • Auto Partitioning • Built in Replicas • Simple Scaling Model Global Secondary Indexes (GSI) • Global Indexes: • Independently Partitioned Indexes • Predictable High Performance at Scale • Sophisticated Scaling Model Controlled with USING VIEW|GSI in CREATE INDEX. Set per Index. Drop and Recreate the index using DROP INDEX & CREATE INDEX to change Index Type.
- 79.
- 80. ©2015 Couchbase Inc.80 A little aboutViews… § Using views you can define – Primary indexes – Simple secondary indexes (most common use case) – Complex secondary, tertiary and composite indexes – Aggregations (reduction) § Built using MapReduce technology – Map and Reduce functions are written in Javascript
- 81. ©2015 Couchbase Inc.81 A little aboutViews… § Captures user-defined key/value pairs about a document into an index § Can execute logic on data during indexing § Able to express relationships too cumbersome to maintain in Key/Value § Incremental indexing essentially pre-computes query results § Higher latency compared to key/value lookups § Common Use Cases – Materialized view: project an alternate view of your document – Complex aggregates – Lightweight reporting
- 82. ©2015 Couchbase Inc.82 Queries run against stale indexes by default § stale=update_after (default if nothing is specified) • always get fastest response • can take two queries to read your own writes § stale=ok • auto update will trigger eventually • might not see your own writes for a few minutes • least frequent updates -> least resource impact § stale=false • Use with “set with persistence” if data needs to be included in view results • BUT be aware of delay it adds, only use when really required
- 83. ©2015 Couchbase Inc.83 Lightweight Reporting Example Document: { “date”: “2015-08-03T16:38:05.835Z”, “source”: “192.168.56.101”, “logtype”: “error”, “message”: “An error occurred during startup” } Map: function(doc, meta) { emit(dateToArray(doc.date), doc.logtype); }
- 84. ©2015 Couchbase Inc.84 Lightweight Reporting Example function(key, values, rereduce) { var response = {"warning" : 0, "error": 0, "fatal" : 0 }; for(i=0; i<values.length; i++) { if (rereduce) { response.warning = response.warning + values[i].warning; response.error = response.error + values[i].error; response.fatal = response.fatal + values[i].fatal; } else { if (values[i] == "warning") response.warning++; if (values[i] == "error" ) response.error++; if (values[i] == "fatal" ) response.fatal++; } } return response; }
- 85. ©2015 Couchbase Inc.85 Lightweight Reporting Example Query: ?group_level=2&startkey=[2010,7]&endkey=[2011,4] {"rows":[ {"key":[2010,7], "value":{"warning":4,"error":2,"fatal":0}}, {"key":[2010,8], "value":{"warning":4,"error":3,"fatal":0}}, {"key":[2010,9], "value":{"warning":4,"error":6,"fatal":0}}, {"key":[2010,10],"value":{"warning":7,"error":6,"fatal":0}}, {"key":[2010,11],"value":{"warning":5,"error":8,"fatal":0}}, {"key":[2010,12],"value":{"warning":2,"error":2,"fatal":0}}, {"key":[2011,1], "value":{"warning":5,"error":1,"fatal":0}}, {"key":[2011,2], "value":{"warning":3,"error":5,"fatal":0}}, {"key":[2011,3], "value":{"warning":4,"error":4,"fatal":0}}, {"key":[2011,4], "value":{"warning":3,"error":6,"fatal":0}} ] }
- 86.
- 87. ©2015 Couchbase Inc.87 Data Service Projector & Router Indexing Service Query ServiceIndex Service Supervisor Index maintenance & Scan coordinator Index#2Index#1 Query Processor cbq-engine Bucket#1 Bucket#2 DCP Stream Index#4Index#3 ... B u c k e t # 2 B u c k e t # 1 Projector and Router: 1 Projector and Router per node 1 stream of changes per bucket per supervisor ForestDB Storage Engine Supervisor 1 Supervisor per node Many indexes per Supervisor MemDB Storage Engine for MOI
- 88. ©2015 Couchbase Inc.88 Types of Indexes § Composite Indexes § Covering Indexes § Filtered Indexes § Function Based Indexes § Sub-Document Indexes § Incremental MapReduceViews § SpatialViews § FullText Indexes § Prepared Statements § N1QL Complete SQL CRUD support: Insert, Delete, Merge, Etc.
- 89. ©2015 Couchbase Inc.89 create index idx_odspilot on ods(DISTINCT ARRAY p.FILEN in p in PILOT END); Array Indexing § Index only required elements or attributes in the array § Benefits are lot more significant for nested arrays/objects § Efficient on Index storage & search time Transportation Company POC • 422,137 documents. • Query2: BEFORE array indexing • Primary index scan • 38.91 seconds. • Query2: AFTER array indexing • Array index scan [DistinctScan] • 8.51 millisecond • Improvement of 4572Times
- 90. ©2015 Couchbase Inc.90 Step by Step – Building indexes § Building and Index 1. Issue “CREATE INDEX …” 2. Place on the “next node” if WITH {“nodes”:[]}not specified – “next node” picked by round robin 3. Create metadata for the index on the node – “next node” picked by round robin 4. If deferred build = true – Mark Index Status = “Pending” – Wait for “BUILD INDEX ...” 5. Else – Mark Index Status = “Pending” – Start DCP Backfill with Projector – When Done – Mark Index Status = “Online”
- 91. ©2015 Couchbase Inc.91 Step by Step – Ingest a Mutation § Ingest a Mutation on Data 1. DCP Feed communicates the mutation to Projector on the node. 2. Projector picks up the mutation for a bucket – Projector strips the mutation to check relevance to existing indexes on each indexer – Projector Sends stripped mutation to indexers on all index service nodes 3. Indexer receive the mutation on each index service node – Queue the mutation – ExtractionWorker Prepares and queues the update to each index. – Persistence Worker persist the mutation to the index
- 92. ©2015 Couchbase Inc.92 Global Secondary Indexes – Storage Options Global Secondary Index Predictable low latency with Query and Key Based Operations, even in presence of large number of indexes Storage Option for GSI Standard Global Secondary Indexes • Optimized for Storage + Memory: efficient spill- to-disk with ForestDB as the storage engine • Advanced IO Performance: New circular-writes that minimize compaction overhead Memory-Optimized Global Secondary Indexes (MOI) • Optimized for Memory: small memory footprint, optimized for lowest latency queries • Faster Indexing: fresh indexes under heavy mutations with lock free index maintenance Controlled with Cluster Settings (Settings Tab). Cluster-wide Setting for all Indexes and Require removing all GSI indexes and index service nodes to change storage options. (New in 4.5)
- 93. ©2015 Couchbase Inc.93 Memory Optimized (MOI) Global Secondary Indexes • Optimized for Memory: small memory footprint, optimized for lowest latency queries • Faster Indexing: fresh indexes under heavy mutations with lock free index maintenance
- 94. ©2015 Couchbase Inc.94 Which to Choose – Standard vs Memory Optimized GSI Standard GSI Memory Optimized GSI Initial Index BuildTimes Slower Faster Ongoing Index Build Times Slower Faster Ability to Keep up with Mutation Rate Lower Higher Memory Demand for Larger Indexes Lower Higher Behavior on OOM (out of memory) Spill to disk Stop processing mutations
- 95. ©2015 Couchbase Inc.95 Indexing & HA § Mirrored Indexes are load balanced and used for HA – Size at least 2 indexes for production – Add more indexes if all requests cannot be handled by a single index to protect against failures
- 96. ©2015 Couchbase Inc.96 Indexes & Partitioning § Why Partition Indexes? – Index may not fit on the node – Fit index to the nodes you have – Distribute Index load to many nodes – Minimize the tree scanned – Large/Tall indexes will take longer to scan § How to Partition Indexes? – By list of values – great for scan latency – ... WHERE type = “customer” – ... WHERE type = “product” – By range – – ... WHERE id BETWEEN 10 AND 20 – ... WHERE id BETWEEN 20 AND 30 – By function – no range scan ability
- 97. ©2015 Couchbase Inc.97 Efficient IO with Circular Writes in Standard GSI Standard Global Secondary Indexes § Reduced Disk IO Requirements – Append-OnlyWrites with frequent full compaction (Version 4.1 & Earlier) – Circular-Reuse Writes with reduced full compactions (New in 4.5: ) – Reused orphaned blocks in the index file – Reduce the need for frequent full-compactions of the index file Index Mutation Append-OnlyWrite CircularWrite File for Global Secondary Indexes Index Mutation Index Mutation
- 98.
- 99. ©2015 Couchbase Inc.99 What is N1QL? § N1QL is a new query language that systematically extends SQL to document-oriented data § N1QL extends SQL to handle data that is: – Nested: Contains nested objects, arrays – Heterogeneous: Schema-optional, non-uniform – Distributed: Partitioned across a cluster
- 100. ©2015 Couchbase Inc.100 N1QL (EXPRESSIVE) § Access to every part of JSON document § Scalar & Aggregate functions § Issue subquery in any expressions § Subqueries § Subqueries in the FROM clause Give developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data.
- 101. ©2015 Couchbase Inc.101 N1QL (POWERFUL) § Access to every part of JSON document § JOINS, Aggregations, standard scalar functions § Aggregation on arrays § NEST & UNNEST operations § Covering Index Give developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data.
- 102. ©2015 Couchbase Inc.102 N1QL (QUERYING) § INSERT § UPDATE § DELETE § MERGE § SELECT § EXPLAIN Give developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data.
- 103. ©2015 Couchbase Inc.103 N1QL (TRANSFORMING & MANIPULATING) § FullTransformation of the data via Query. § INSERT § INSERT single & multiple documents § INSERT result a SELECT statement § DELETE documents based on complex filter § UPDATE any part of JSON document & use complex filter. § MERGE two sets of documents using traditional MERGE statement § SUBQUERIES Give developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data.
- 104. ©2015 Couchbase Inc.104 Optimizing Query Speed § There are four options for N1QL queries: Primary Index Secondary Index (GSI or MOI) Covered Index “ON KEYS”
- 105. ©2015 Couchbase Inc.105 Query Execution Flow 1. Application submits N1QL query 2. Query is parsed, analyzed and plan is created 1 2
- 106. ©2015 Couchbase Inc.106 Query Execution Flow 3. Query Service makes request to Index Service 4. Index Service returns document keys and data 3 4
- 107. ©2015 Couchbase Inc.107 Query Execution Flow 5. If Covering Index, skip step 6 6. If filtering is required, fetch documents from Data Service56
- 108. ©2015 Couchbase Inc.108 Query Execution Flow 7. Apply final logic (e.g. SORT, ORDER BY) 8. Return formatted results to application 7 8
- 109. ©2015 Couchbase Inc.109 Inside a Query Service (without covering index) Client FetchParse Plan Join Filter Pre-Aggregate Offset Limit ProjectSortAggregateScan Query Service Index Servic e Data Servic e Data-parallel — Query is N data streams over N cores* Memory-based Pluggable architecture — datastore, index… Request Response
- 110. ©2015 Couchbase Inc.110 Query Consistency Strict Request-Time Consistency (request_plus) Query execution is delayed until all indexes process mutations up toT3 RYOW Consistency (at_plus) Query execution is delayed until all indexes process mutations up toT1 T1: insert (k1, v1) T3: issue query on (k1,v1) T2: do other business logic computation … …
- 111. ©2015 Couchbase Inc.111 Joins (Referential)
- 112. ©2015 Couchbase Inc.112 Joins (Referential)
- 113. ©2015 Couchbase Inc.113 Joins (Referential)
- 114. ©2015 Couchbase Inc.114 Joins (Referential)
- 115. ©2015 Couchbase Inc.115 Document-oriented Extensions § Nested – Paths—user.profile.email, children[0], children[0:2] – NEST, UNNEST – Ranging—EVERY child.age > 5 FOR child IN children END – Transformations—SELECT {“name”: first_name || last_name} § Non-uniform – IS MISSING – Type checking and conversion § Distributed – Direct lookup—USE KEYS – Efficient joins—ON KEYS
- 116. ©2015 Couchbase Inc.116 SELECT statement SELECT customers.id, customers.NAME.lastname, customers.NAME.firstname Sum(orderline.amount) FROM orders UNNEST orders.lineitems AS orderline JOIN customers ON KEYS orders.custid WHERE customers.state = 'NY' GROUP BY customers.id, customers.NAME.lastname HAVING sum(orderline.amount) > 10000 ORDER BY sum(orderline.amount) DESC Dotted sub-document reference Names are CASE-SENSITIVE UNNEST to flatten the arrays JOINS with Document KEY of customers
- 117. ©2015 Couchbase Inc.117 UNNEST example: Customer Document { "C_ZIP" : "828011111", "C_STATE" : "vt", "C_FIRST" : "ykfdbqku", "C_CREDIT" : "GC", "C_DELIVERY_CNT" : 0, "C_W_ID" : 1, "C_CITY" : "quhpismkzumehqhr", "C_STREET_1" : "rmtxadlsxqefdcwf", "C_D_ID" : 1, "ORDERS" : [ { "ORDER_LINE" : [ { "OL_AMOUNT" : 0, "OL_DELIVERY_D" : "2015-02-11T14:55:25.480Z", "OL_DIST_INFO" : "yptiwgjdelfxmathbjzirvye", "OL_I_ID" : 35828, "OL_SUPPLY_W_ID" : 1, "OL_QUANTITY" : 5 }, { "OL_AMOUNT" : 0, "OL_DELIVERY_D" : "2015-02-11T14:55:25.480Z", "OL_DIST_INFO" : "dxhqulhcgksjgqsicujzqhdb", "OL_I_ID" : 26024, "OL_SUPPLY_W_ID" : 1, "OL_QUANTITY" : 5 }, } ….
- 118. ©2015 Couchbase Inc.118 UNNEST example: N1QL Code { "C_ZIP" : "828011111", "C_STATE" : "vt", "C_FIRST" : "ykfdbqku", "C_CREDIT" : "GC", "C_DELIVERY_CNT" : 0, "C_W_ID" : 1, "C_CITY" : "quhpismkzumehqhr", "C_STREET_1" : "rmtxadlsxqefdcwf", "C_D_ID" : 1, "ORDERS" : [ { "ORDER_LINE" : [ { "OL_AMOUNT" : 0, "OL_DELIVERY_D" : "2015-02-11T14:55:25.480Z", "OL_DIST_INFO" : "yptiwgjdelfxmathbjzirvye", "OL_I_ID" : 35828, "OL_SUPPLY_W_ID" : 1, "OL_QUANTITY" : 5 }, { "OL_AMOUNT" : 0, "OL_DELIVERY_D" : "2015-02-11T14:55:25.480Z", "OL_DIST_INFO" : "dxhqulhcgksjgqsicujzqhdb", "OL_I_ID" : 26024, "OL_SUPPLY_W_ID" : 1, "OL_QUANTITY" : 5 }, } …. SELECT COUNT(my_order_line)AS total_orders, MAX(my_order_line.ol_delivery_d)AS max_delivery_date, MAX(my_order_line.ol_quantity)AS max_order_quantity, MAX(my_orders.o_entry_d) AS max_customer_entry, MAX(my_orders.o_ol_cnt) AS max_orderline_entry, COUNT(customer)AS total_customers FROM CUSTOMER MY_CUSTOMER UNNEST ORDERS AS my_orders UNNEST my_orders.order_line AS my_order_line ;
- 119. ©2015 Couchbase Inc.119 Named Prepared Statement url="http://localhost:8093/query" s = requests.Session() s.keep_alive = True s.auth = ('Administrator','password') query = {'statement':’prepare select * from `beer-sample` where type=’beer’ and name = 'Guinness’} r = s.post(url, data=query, stream=False) prepared = str(r.json()['results'][0]['name']) query = {'prepared': '"' + prepared + '"' } for i in range (0, 5000): r = s.post(url, data=query, stream=False) print i, r.json()['metrics']['executionTime']
- 120. ©2015 Couchbase Inc.120 Named Prepared Statement url="http://localhost:8093/query" s = requests.Session() s.keep_alive = True s.auth = ('Administrator','password') query = {'statement':'prepare select * from `beer-sample` where name = [$1]’} r = s.post(url, data=query, stream=False) prepared = str(r.json()['results'][0]['name']) for i in range (0, 5): query={'prepared': '"' + prepared + '"', 'args': '["old_hat_brewery"]' } r = s.post(url, data=query, stream=False) print i, r.json()['metrics']['executionTime'] Prepare Once Bind Values Many Times
- 121. ©2015 Couchbase Inc.121 Query Workbench – Query Editing Query Editor Autocomplete Syntax highlighting Multi-line formatting History Create, edit, save, run Results JSON /Table /Tree
- 122. ©2015 Couchbase Inc.122 Query Workbench – Schema Browsing Schema Inferred Document types Element types Frequency Sample data Bucket Schema Indexes
- 123. ©2015 Couchbase Inc.123 Query Workbench – Query Monitoring Catalogs Vitals Active Requests Prepared Statements Completed Requests Interfaces QueryWorkbench CLI (Query Shell) REST API
- 124. ©2015 Couchbase Inc.124 Monitoring: Active requests § List / Delete requests currently being run by the query service § Through N1QL – SELECT * FROM system:active_requests – DELETE FROM system:active_requests WHERE… § Through REST – GET http://localhost:8093/admin/active_requests – GET http://localhost:8093/admin/active_requests/<request_id> – DELETE http://localhost:8093/admin/active_requests/<request_id>
- 125. ©2015 Couchbase Inc.125 Monitoring: Prepared statements § List / Delete requests prepared on the query node § Through N1QL – SELECT * FROM system:prepareds – DELETE FROM system:prepareds WHERE… § Through REST – GET http://localhost:8093/admin/prepareds – GET http://localhost:8093/admin/prepareds/<request_id> – DELETE http://localhost:8093/admin/prepareds/<request_id>
- 126. ©2015 Couchbase Inc.126 Monitoring: Completed requests § List / Delete completed requests deemed to be of high cost § Through N1QL – SELECT * FROM system:completed_requests – DELETE FROM system:completed_requests where… § Through REST – GET http://localhost:8093/admin/completed_requests – GET http://localhost:8093/admin/completed_requests/<request_id> – DELETE http://localhost:8093/admin/completed_requests/<request_id> § Provide an overall health picture of the query service – Using REST: GET http://localhost:8093/admin/vitals
- 127.
- 128.
- 129. ©2015 Couchbase Inc.129 Key Challenges When Building Mobile Apps According to study done by Bock & Company Enterprise Developers Rank Critical Requirements for Success in the Coming Mobile App Explosion
- 130. ©2015 Couchbase Inc.130 What is Couchbase Mobile • Removed continual network dependency • Faster development cycles • Less long term maintenance than traditional solutions • Enterprise class mobile/embedded NoSQL database + sync platform • Fast and consistent access to data Built-in enterprise level security throughout the entire stack includes user authentication, user and role based data access control (RBAC), secure transport (TLS), and 256-bit AES full database encryption. SECURITY
- 131. ©2015 Couchbase Inc.131 Couchbase Lite Overview § Cross-platform support for all major operating systems and platforms § Built native from the ground up § 500kb for most platforms § 256-bit AES full database encryption 131 Couchbase Lite Embedded Database
- 132. ©2015 Couchbase Inc.132 Sync Gateway Overview § Synchronize data between Couchbase Lite and Couchbase Server § REST, Stream, Batch, and Event APIs § Pluggable authentication § Fine grained user and role based access control § Elastically scalable in real-time 132 Sync Gateway Synchronization
- 133. ©2015 Couchbase Inc.133 Couchbase Server Overview § Scale easily to thousands of nodes § Consistent high performance that supports millions of concurrent users § Flexible JSON data model § 24x365 always-on availability 133 Couchbase Server Database Server
- 134. ©2015 Couchbase Inc.134 End-to-End Security ü Pluggable authentication ü Fine grained user and role based access control ü Transport layer security (TLS) ü 256-bit AES full database encryption on device ü File system encryption in the cloud Security
- 135. ©2015 Couchbase Inc.135 Who s using Couchbase Mobile today
- 136. ©2015 Couchbase Inc.136 Common Mobile Use CasesCommon Mobile Use Cases
- 137. ©2015 Couchbase Inc.137 Offline Data Access The Problem • When an app doesn’t work offline, users are cut off from important transactions • Mobile apps need to work regardless of network connection How Couchbase Mobile helps • Couchbase Lite stores data locally so your app isn’t dependent on the network • If a network connection is lost, the database continues to operate and is automatically synced with the cloud when the network is restored
- 138. ©2015 Couchbase Inc.138 Local Caching The Problem • Today’s mobile applications require fast access to data • Slow access to data results in slow mobile apps, unhappy users and loss of productivity How Couchbase Mobile helps • By storing and caching application data locally in the embedded Couchbase Lite database, you eliminate the need to constantly request data from the server • You can synchronize data automatically with Sync Gateway
- 139. ©2015 Couchbase Inc.139 Digitization of Paper Assets The Problem • Reducing operational time and costs is critical for any business, and mobile application deployments are a key component of making it happen • By digitizing paper assets, you save time and resources, reduce storage, and cut logistics costs How Couchbase Mobile helps • Store paper assets with Couchbase Mobile, both locally and in the cloud • Your data is securely synced between devices and the cloud
- 140. ©2015 Couchbase Inc.140 Content Delivery & Storage The Problem • Guaranteed delivery and storage of assets is crucial for a great user experience • Users need real-time data updates How Couchbase Mobile helps • Couchbase Mobile gives you the ability to store and access data locally so content is always available • Data can be synced between the cloud and device, allowing business-critical updates to be reflected in real-time
- 141. ©2015 Couchbase Inc.141 Real-time Asset Tracking The Problem • Real-time asset tracking is a key requirement in mobile applications across many industries • Tracking, storing, and delivering real-time updates can be tricky to manage How Couchbase Mobile helps • Asset tracking data can be shared with supervisors and/or across field employees • Deliver updates on shipments, routing, service orders, ETAs, or anything else in real- time with Couchbase Mobile
- 142. ©2015 Couchbase Inc.142 Peer-to-Peer Usage The Problem • Without a network connection, devices can’t talk to each other • In isolated environments like retail stores, airplanes, the field, or any other places where the network isn’t reliable, this can cause lost revenue and productivity How Couchbase Mobile helps • With Couchbase Mobile, connect devices directly to each other with peer-to-peer replication • Apps can connect to exchange data and will continue to work regardless of network availability
- 143. ©2015 Couchbase Inc.143 User Data/Preferences Storage The Problem • Storing user data and preferences across devices and the cloud is complex for games, CRM systems, e-commerce apps, or any other app • Making sure user information is secure, both on-device and in transport, is difficult How Couchbase Mobile helps • Couchbase Mobile gives your users a consistent experience across all of their devices • Securely store and sync user data like preferences, profiles, sign-in, game state and more across devices
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- 145. ©2015 Couchbase Inc.145 Couchbase Server and Intel’s high-performance processors and storage subsystems combine to power millions of operations/sec for the world’s largest customer 360 systems and profile stores, popular online games, and more. Additional information about Couchbase and Intel can be found here: http://www.couchbase.com/partners/intel Power with Intel
- 146. ©2015 Couchbase Inc.146 Consulting and Expert Services Packaged Services – Quick Start – Architecture Reviews – Health Checks – Upgrade Services – Managed Services Custom Consulting – Migration – Application Integration – Big-Data Integration, Analytics, Reporting – Others Email inquiries: Services@couchbase.com OR Contact your Account Representative
- 147. ©2015 Couchbase Inc.147 Couchbase Hands-OnTraining For System Admins: § CS300 Couchbase NoSQL Server Administration, 4-day For Developers: § CD220 Developing Couchbase NoSQL Applications, 3-day For Anyone: § Free Online On-Demand Courses: http://training.couchbase.com/online Email inquiries: training@couchbase.com
- 148. ©2015 Couchbase Inc.148 Additional Resources 148 § Getting Started: http://www.couchbase.com/download § Couchbase Labs: http://github.com/couchbaselabs § Developer Portal: http://developer.couchbase.com § General Docs: http://docs.couchbase.com § Query Portal: http://query.couchbase.com § Blog: http://blog.couchbase.com § Forum: http://forums.couchbase.com § Sample Applications: § https://github.com/couchbaselabs?utf8=%E2%9C%93&query=try § https://github.com/couchbaselabs?utf8=%E2%9C%93&query=beer