http://git-wip-us.apache.org/repos/asf/incubator-gearpump/blob/cc0578e5/docs/docs/dev/dev-rest-api.md ---------------------------------------------------------------------- diff --git a/docs/docs/dev/dev-rest-api.md b/docs/docs/dev/dev-rest-api.md deleted file mode 100644 index 85c4706..0000000 --- a/docs/docs/dev/dev-rest-api.md +++ /dev/null @@ -1,1083 +0,0 @@ -## Authentication. - -For all REST API calls, We need authentication by default. If you don't want authentication, you can disable them. - -### How to disable Authentication -To disable Authentication, you can set `gearpump-ui.gearpump.ui-security.authentication-enabled = false` -in gear.conf, please check [UI Authentication](../deployment/deployment-ui-authentication) for details. - -### How to authenticate if Authentication is enabled. - -#### For User-Password based authentication - -If Authentication is enabled, then you need to login before calling REST API. - - :::bash - curl -X POST --data username=admin --data password=admin --cookie-jar outputAuthenticationCookie.txt http://127.0.0.1:8090/login - - -This will use default user "admin:admin" to login, and store the authentication cookie to file outputAuthenticationCookie.txt. - -In All subsequent Rest API calls, you need to add the authentication cookie. For example - - :::bash - curl --cookie outputAuthenticationCookie.txt http://127.0.0.1/api/v1.0/master - - -for more information, please check [UI Authentication](../deployment/deployment-ui-authentication). - -#### For OAuth2 based authentication - -For OAuth2 based authentication, it requires you to have an access token in place. - -Different OAuth2 service provider have different way to return an access token. - -**For Google**, you can refer to [OAuth Doc](https://developers.google.com/identity/protocols/OAuth2). - -**For CloudFoundry UAA**, you can use the uaac command to get the access token. - - :::bash - $ uaac target http://login.gearpump.gotapaas.eu/ - $ uaac token get <user_email_address> - - ### Find access token - $ uaac context - - [0]*[http://login.gearpump.gotapaas.eu] - - [0]*[<user_email_address>] - user_id: 34e33a79-42c6-479b-a8c1-8c471ff027fb - client_id: cf - token_type: bearer - access_token: eyJhbGciOiJSUzI1NiJ9.eyJqdGkiOiI - expires_in: 599 - scope: password.write openid cloud_controller.write cloud_controller.read - jti: 74ea49e4-1001-4757-9f8d-a66e52a27557 - - -For more information on uaac, please check [UAAC guide](https://docs.cloudfoundry.org/adminguide/uaa-user-management.html) - -Now, we have the access token, then let's login to Gearpump UI server with this access token: - - :::bash - ## Please replace cloudfoundryuaa with actual OAuth2 service name you have configured in gear.conf - curl -X POST --data accesstoken=eyJhbGciOiJSUzI1NiJ9.eyJqdGkiOiI --cookie-jar outputAuthenticationCookie.txt http://127.0.0.1:8090/login/oauth2/cloudfoundryuaa/accesstoken - - -This will use user `user_email_address` to login, and store the authentication cookie to file outputAuthenticationCookie.txt. - -In All subsequent Rest API calls, you need to add the authentication cookie. For example - - :::bash - curl --cookie outputAuthenticationCookie.txt http://127.0.0.1/api/v1.0/master - - -**NOTE:** You can default the default permission level for OAuth2 user. for more information, -please check [UI Authentication](../deployment/deployment-ui-authentication). - -## Query version - -### GET version - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/version - - -Sample Response: - - :::bash - {{GEARPUMP_VERSION}} - - -## Master Service - -### GET api/v1.0/master -Get information of masters - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/master - - -Sample Response: - - :::json - { - "masterDescription": { - "leader":{"host":"[email protected]","port":3000}, - "cluster":[{"host":"127.0.0.1","port":3000}] - "aliveFor": "642941", - "logFile": "/Users/foobar/gearpump/logs", - "jarStore": "jarstore/", - "masterStatus": "synced", - "homeDirectory": "/Users/foobar/gearpump" - } - } - - -### GET api/v1.0/master/applist -Query information of all applications - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/master/applist - - -Sample Response: - - :::json - { - "appMasters": [ - { - "status": "active", - "appId": 1, - "appName": "wordCount", - "appMasterPath": "akka.tcp://[email protected]:52212/user/daemon/appdaemon1/$c", - "workerPath": "akka.tcp://[email protected]:3000/user/Worker0", - "submissionTime": "1450758114766", - "startTime": "1450758117294", - "user": "lisa" - } - ] - } - -### GET api/v1.0/master/workerlist -Query information of all workers - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/master/workerlist - -Sample Response: - - :::json - [ - { - "workerId": "1", - "state": "active", - "actorPath": "akka.tcp://[email protected]:3000/user/Worker0", - "aliveFor": "431565", - "logFile": "logs/", - "executors": [ - { - "appId": 1, - "executorId": -1, - "slots": 1 - }, - { - "appId": 1, - "executorId": 0, - "slots": 1 - } - ], - "totalSlots": 1000, - "availableSlots": 998, - "homeDirectory": "/usr/lisa/gearpump/", - "jvmName": "11788@lisa" - }, - { - "workerId": "0", - "state": "active", - "actorPath": "akka.tcp://[email protected]:3000/user/Worker1", - "aliveFor": "431546", - "logFile": "logs/", - "executors": [ - { - "appId": 1, - "executorId": 1, - "slots": 1 - } - ], - "totalSlots": 1000, - "availableSlots": 999, - "homeDirectory": "/usr/lisa/gearpump/", - "jvmName": "11788@lisa" - } - ] - -### GET api/v1.0/master/config -Get the configuration of all masters - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/master/config - -Sample Response: - - :::json - { - "extensions": [ - "akka.contrib.datareplication.DataReplication$" - ] - "akka": { - "loglevel": "INFO" - "log-dead-letters": "off" - "log-dead-letters-during-shutdown": "off" - "actor": { - ## Master forms a akka cluster - "provider": "akka.cluster.ClusterActorRefProvider" - } - "cluster": { - "roles": ["master"] - "auto-down-unreachable-after": "15s" - } - "remote": { - "log-remote-lifecycle-events": "off" - } - } - } - - -### GET api/v1.0/master/metrics/<query_path>?readLatest=<true|false> -Get the master node metrics. - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/master/metrics/master?readLatest=true - -Sample Response: - - :::bash - { - "path" - : - "master", "metrics" - : - [{ - "time": "1450758725070", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "master:memory.heap.used", "value": "59764272"} - }, { - "time": "1450758725070", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "master:thread.daemon.count", "value": "18"} - }, { - "time": "1450758725070", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "master:memory.total.committed", - "value": "210239488" - } - }, { - "time": "1450758725070", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "master:memory.heap.max", "value": "880017408"} - }, { - "time": "1450758725070", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "master:memory.total.max", "value": "997457920"} - }, { - "time": "1450758725070", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "master:memory.heap.committed", - "value": "179830784" - } - }, { - "time": "1450758725070", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "master:memory.total.used", "value": "89117352"} - }, { - "time": "1450758725070", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "master:thread.count", "value": "28"} - }] - } - - -### POST api/v1.0/master/submitapp -Submit a streaming job jar to Gearpump cluster. It functions like command line - - :::bash - gear app -jar xx.jar -conf yy.conf -executors 1 <command line arguments> - - -Required MIME type: "multipart/form-data" - -Required post form fields: - -1. field name "jar", job jar file. - -Optional post form fields: - -1. "configfile", configuration file, in UTF8 format. -2. "configstring", text body of configuration file, in UTF8 format. -3. "executorcount", The count of JVM process to start across the cluster for this application job -4. "args", command line arguments for this job jar. - -Example html: - - :::html - <form id="submitapp" action="http://127.0.0.1:8090/api/v1.0/master/submitapp"; - method="POST" enctype="multipart/form-data"> - - Job Jar (*.jar) [Required]: <br/> - <input type="file" name="jar"/> <br/> <br/> - - Config file (*.conf) [Optional]: <br/> - <input type="file" name="configfile"/> <br/> <br/> - - Config String, Config File in string format. [Optional]: <br/> - <input type="text" name="configstring" value="a.b.c.d=1"/> <br/><br/> - - Executor count (integer, how many process to start for this streaming job) [Optional]: <br/> - <input type="text" name="executorcount" value="1"/> <br/><br/> - - Application arguments (String) [Optional]: <br/> - <input type="text" name="args" value=""/> <br/><br/> - - <input type="submit" value="Submit"/> - - </table> - - </form> - -### POST api/v1.0/master/submitstormapp -Submit a storm jar to Gearpump cluster. It functions like command line - - :::bash - storm app -jar xx.jar -conf yy.yaml <command line arguments> - -Required MIME type: "multipart/form-data" - -Required post form fields: - -1. field name "jar", job jar file. - -Optional post form fields: - -1. "configfile", .yaml configuration file, in UTF8 format. -2. "args", command line arguments for this job jar. - -Example html: - - :::html - <form id="submitstormapp" action="http://127.0.0.1:8090/api/v1.0/master/submitstormapp"; - method="POST" enctype="multipart/form-data"> - - Job Jar (*.jar) [Required]: <br/> - <input type="file" name="jar"/> <br/> <br/> - - Config file (*.yaml) [Optional]: <br/> - <input type="file" name="configfile"/> <br/> <br/> - - Application arguments (String) [Optional]: <br/> - <input type="text" name="args" value=""/> <br/><br/> - - <input type="submit" value="Submit"/> - - </table> - - </form> - - -## Worker service - -### GET api/v1.0/worker/<workerId> -Query worker information. - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/worker/0 - -Sample Response: - - :::json - { - "workerId": "0", - "state": "active", - "actorPath": "akka.tcp://[email protected]:3000/user/Worker1", - "aliveFor": "831069", - "logFile": "logs/", - "executors": [ - { - "appId": 1, - "executorId": 1, - "slots": 1 - } - ], - "totalSlots": 1000, - "availableSlots": 999, - "homeDirectory": "/usr/lisa/gearpump/", - "jvmName": "11788@lisa" - } - -### GET api/v1.0/worker/<workerId>/config -Query worker config - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/worker/0/config - - -Sample Response: - - :::json - { - "extensions": [ - "akka.contrib.datareplication.DataReplication$" - ] - "akka": { - "loglevel": "INFO" - "log-dead-letters": "off" - "log-dead-letters-during-shutdown": "off" - "actor": { - ## Master forms a akka cluster - "provider": "akka.cluster.ClusterActorRefProvider" - } - "cluster": { - "roles": ["master"] - "auto-down-unreachable-after": "15s" - } - "remote": { - "log-remote-lifecycle-events": "off" - } - } - } - - -### GET api/v1.0/worker/<workerId>/metrics/<query_path>?readLatest=<true|false> -Get the worker node metrics. - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/worker/0/metrics/worker?readLatest=true - -Sample Response: - - :::json - { - "path" - : - "worker", "metrics" - : - [{ - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.total.used", - "value": "152931440" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker1:thread.daemon.count", "value": "18"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.heap.used", - "value": "123139640" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.total.max", - "value": "997457920" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.heap.committed", - "value": "179830784" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker0:thread.count", "value": "28"} - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker0:memory.heap.max", "value": "880017408"} - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker1:memory.heap.max", "value": "880017408"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.total.committed", - "value": "210239488" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.total.used", - "value": "152931440" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker1:thread.count", "value": "28"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.total.max", - "value": "997457920" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.heap.committed", - "value": "179830784" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.total.committed", - "value": "210239488" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker0:thread.daemon.count", "value": "18"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.heap.used", - "value": "123139640" - } - }] - } - - -## Supervisor Service - -Supervisor service allows user to add or remove a worker machine. - -### POST api/v1.0/supervisor/status -Query whether the supervisor service is enabled. If Supervisor service is disabled, you are not allowed to use API like addworker/removeworker. - -Example: - - :::bash - curl -X POST [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/supervisor/status - -Sample Response: - - :::json - {"enabled":true} - - -### GET api/v1.0/supervisor -Get the supervisor path - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/supervisor - - -Sample Response: - - :::json - {path: "supervisor actor path"} - -### POST api/v1.0/supervisor/addworker/<worker-count> -Add workerCount new workers in the cluster. It will use the low level resource scheduler like -YARN to start new containers and then boot Gearpump worker process. - -Example: - - :::bash - curl -X POST [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/supervisor/addworker/2 - - - -Sample Response: - - :::json - {success: true} - -### POST api/v1.0/supervisor/removeworker/<worker-id> -Remove single worker instance by specifying a worker Id. - -**NOTE:* Use with caution! - -**NOTE:** All executors JVMs under this worker JVM will also be destroyed. It will trigger failover for all -applications that have executor started under this worker. - -Example: - - :::bash - curl -X POST [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/supervisor/removeworker/3 - - -Sample Response: - - :::json - {success: true} - -## Application service - -### GET api/v1.0/appmaster/<appId>?detail=<true|false> -Query information of an specific application of Id appId - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/appmaster/1?detail=true - -Sample Response: - - :::json - { - "appId": 1, - "appName": "wordCount", - "processors": [ - [ - 0, - { - "id": 0, - "taskClass": "org.apache.gearpump.streaming.examples.wordcount.Split", - "parallelism": 1, - "description": "", - "taskConf": { - "_config": {} - }, - "life": { - "birth": "0", - "death": "9223372036854775807" - }, - "executors": [ - 1 - ], - "taskCount": [ - [ - 1, - { - "count": 1 - } - ] - ] - } - ], - [ - 1, - { - "id": 1, - "taskClass": "org.apache.gearpump.streaming.examples.wordcount.Sum", - "parallelism": 1, - "description": "", - "taskConf": { - "_config": {} - }, - "life": { - "birth": "0", - "death": "9223372036854775807" - }, - "executors": [ - 0 - ], - "taskCount": [ - [ - 0, - { - "count": 1 - } - ] - ] - } - ] - ], - "processorLevels": [ - [ - 0, - 0 - ], - [ - 1, - 1 - ] - ], - "dag": { - "vertexList": [ - 0, - 1 - ], - "edgeList": [ - [ - 0, - "org.apache.gearpump.partitioner.HashPartitioner", - 1 - ] - ] - }, - "actorPath": "akka.tcp://[email protected]:52212/user/daemon/appdaemon1/$c/appmaster", - "clock": "1450759382430", - "executors": [ - { - "executorId": 0, - "executor": "akka.tcp://[email protected]:52240/remote/akka.tcp/[email protected]:52212/user/daemon/appdaemon1/$c/appmaster/executors/0#-1554950276", - "workerId": "1", - "status": "active" - }, - { - "executorId": 1, - "executor": "akka.tcp://[email protected]:52241/remote/akka.tcp/[email protected]:52212/user/daemon/appdaemon1/$c/appmaster/executors/1#928082134", - "workerId": "0", - "status": "active" - }, - { - "executorId": -1, - "executor": "akka://app1-executor-1/user/daemon/appdaemon1/$c/appmaster", - "workerId": "1", - "status": "active" - } - ], - "startTime": "1450758117306", - "uptime": "1268472", - "user": "lisa", - "homeDirectory": "/usr/lisa/gearpump/", - "logFile": "logs/", - "historyMetricsConfig": { - "retainHistoryDataHours": 72, - "retainHistoryDataIntervalMs": 3600000, - "retainRecentDataSeconds": 300, - "retainRecentDataIntervalMs": 15000 - } - } - - -### DELETE api/v1.0/appmaster/<appId> -shutdown application appId - -### GET api/v1.0/appmaster/<appId>/stallingtasks -Query list of unhealthy tasks of an specific application of Id appId - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/appmaster/2/stallingtasks - - -Sample Response: - - :::json - { - "tasks": [ - { - "processorId": 0, - "index": 0 - } - ] - } - - -### GET api/v1.0/appmaster/<appId>/config -Query the configuration of specific application appId - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/appmaster/1/config - - -Sample Response: - - :::json - { - "gearpump" : { - "appmaster" : { - "extraClasspath" : "", - "vmargs" : "-server -Xms512M -Xmx1024M -Xss1M -XX:+HeapDumpOnOutOfMemoryError -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=80 -XX:+UseParNewGC -XX:NewRatio=3" - }, - "cluster" : { - "masters" : [ - "127.0.0.1:3000" - ] - }, - "executor" : { - "extraClasspath" : "", - "vmargs" : "-server -Xms512M -Xmx1024M -Xss1M -XX:+HeapDumpOnOutOfMemoryError -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=80 -XX:+UseParNewGC -XX:NewRatio=3" - }, - "jarstore" : { - "rootpath" : "jarstore/" - }, - "log" : { - "application" : { - "dir" : "logs" - }, - "daemon" : { - "dir" : "logs" - } - }, - "metrics" : { - "enabled" : true, - "graphite" : { - "host" : "127.0.0.1", - "port" : 2003 - }, - "logfile" : {}, - "report-interval-ms" : 15000, - "reporter" : "akka", - "retainHistoryData" : { - "hours" : 72, - "intervalMs" : 3600000 - }, - "retainRecentData" : { - "intervalMs" : 15000, - "seconds" : 300 - }, - "sample-rate" : 10 - }, - "netty" : { - "base-sleep-ms" : 100, - "buffer-size" : 5242880, - "flush-check-interval" : 10, - "max-retries" : 30, - "max-sleep-ms" : 1000, - "message-batch-size" : 262144 - }, - "netty-dispatcher" : "akka.actor.default-dispatcher", - "scheduling" : { - "scheduler-class" : "org.apache.gearpump.cluster.scheduler.PriorityScheduler" - }, - "serializers" : { - "[B" : "", - "[C" : "", - "[D" : "", - "[F" : "", - "[I" : "", - "[J" : "", - "[Ljava.lang.String;" : "", - "[S" : "", - "[Z" : "", - "org.apache.gearpump.Message" : "org.apache.gearpump.streaming.MessageSerializer", - "org.apache.gearpump.streaming.task.Ack" : "org.apache.gearpump.streaming.AckSerializer", - "org.apache.gearpump.streaming.task.AckRequest" : "org.apache.gearpump.streaming.AckRequestSerializer", - "org.apache.gearpump.streaming.task.LatencyProbe" : "org.apache.gearpump.streaming.LatencyProbeSerializer", - "org.apache.gearpump.streaming.task.TaskId" : "org.apache.gearpump.streaming.TaskIdSerializer", - "scala.Tuple1" : "", - "scala.Tuple2" : "", - "scala.Tuple3" : "", - "scala.Tuple4" : "", - "scala.Tuple5" : "", - "scala.Tuple6" : "", - "scala.collection.immutable.$colon$colon" : "", - "scala.collection.immutable.List" : "" - }, - "services" : { - # gear.conf: 112 - "host" : "127.0.0.1", - # gear.conf: 113 - "http" : 8090, - # gear.conf: 114 - "ws" : 8091 - }, - "task-dispatcher" : "akka.actor.pined-dispatcher", - "worker" : { - # reference.conf: 100 - # # How many slots each worker contains - "slots" : 100 - } - } - } - - -### GET api/v1.0/appmaster/<appId>/metrics/<query_path>?readLatest=<true|false>&aggregator=<aggregator_class> -Query metrics information of a specific application appId -Filter metrics with path metrics path - -aggregator points to a aggregator class, which will aggregate on the current metrics, and return a smaller set. - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/appmaster/1/metrics/app1?readLatest=true&aggregator=org.apache.gearpump.streaming.metrics.ProcessorAggregator - - -Sample Response: - - :::json - { - "path" - : - "worker", "metrics" - : - [{ - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.total.used", - "value": "152931440" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker1:thread.daemon.count", "value": "18"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.heap.used", - "value": "123139640" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.total.max", - "value": "997457920" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.heap.committed", - "value": "179830784" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker0:thread.count", "value": "28"} - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker0:memory.heap.max", "value": "880017408"} - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker1:memory.heap.max", "value": "880017408"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.total.committed", - "value": "210239488" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker0:memory.total.used", - "value": "152931440" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker1:thread.count", "value": "28"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.total.max", - "value": "997457920" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.heap.committed", - "value": "179830784" - } - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.total.committed", - "value": "210239488" - } - }, { - "time": "1450759137860", - "value": {"$type": "org.apache.gearpump.metrics.Metrics.Gauge", "name": "worker0:thread.daemon.count", "value": "18"} - }, { - "time": "1450759137860", - "value": { - "$type": "org.apache.gearpump.metrics.Metrics.Gauge", - "name": "worker1:memory.heap.used", - "value": "123139640" - } - }] - } - - -### GET api/v1.0/appmaster/<appId>/errors -Get task error messages - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/appmaster/1/errors - - -Sample Response: - - :::json - {"time":"0","error":null} - - -### POST api/v1.0/appmaster/<appId>/restart -Restart the application - -## Executor Service - -### GET api/v1.0/appmaster/<appId>/executor/<executorid>/config -Get executor config - -Example: - - :::bash - curl http://127.0.0.1:8090/api/v1.0/appmaster/1/executor/1/config - - -Sample Response: - - :::json - { - "extensions": [ - "akka.contrib.datareplication.DataReplication$" - ] - "akka": { - "loglevel": "INFO" - "log-dead-letters": "off" - "log-dead-letters-during-shutdown": "off" - "actor": { - ## Master forms a akka cluster - "provider": "akka.cluster.ClusterActorRefProvider" - } - "cluster": { - "roles": ["master"] - "auto-down-unreachable-after": "15s" - } - "remote": { - "log-remote-lifecycle-events": "off" - } - } - } - - -### GET api/v1.0/appmaster/<appId>/executor/<executorid> -Get executor information. - -Example: - - :::bash - curl [--cookie outputAuthenticationCookie.txt] http://127.0.0.1:8090/api/v1.0/appmaster/1/executor/1 - - -Sample Response: - - :::json - { - "id": 1, - "workerId": "0", - "actorPath": "akka.tcp://[email protected]:52241/remote/akka.tcp/[email protected]:52212/user/daemon/appdaemon1/$c/appmaster/executors/1", - "logFile": "logs/", - "status": "active", - "taskCount": 1, - "tasks": [ - [ - 0, - [ - { - "processorId": 0, - "index": 0 - } - ] - ] - ], - "jvmName": "21304@lisa" - } -
http://git-wip-us.apache.org/repos/asf/incubator-gearpump/blob/cc0578e5/docs/docs/dev/dev-storm.md ---------------------------------------------------------------------- diff --git a/docs/docs/dev/dev-storm.md b/docs/docs/dev/dev-storm.md deleted file mode 100644 index e60b505..0000000 --- a/docs/docs/dev/dev-storm.md +++ /dev/null @@ -1,214 +0,0 @@ -Gearpump provides **binary compatibility** for Apache Storm applications. That is to say, users could easily grab an existing Storm jar and run it -on Gearpump. This documentation illustrates Gearpump's compatibility with Storm. - -## What Storm features are supported on Gearpump - -### Storm 0.9.x - -| Feature | Support | -| ------- | ------- | -| basic topology | yes | -| DRPC | yes | -| multi-lang | yes | -| storm-kafka | yes | -| Trident | no | - -### Storm 0.10.x - -| Feature | Support | -| ----------- | -------------| -| basic topology | yes | -| DRPC | yes | -| multi-lang | yes | -| storm-kafka | yes | -| storm-hdfs| yes | -| storm-hbase | yes | -| storm-hive | yes | -| storm-jdbc | yes | -| storm-redis | yes | -| flux | yes | -| storm-eventhubs | not verified | -| Trident | no | - -### At Least Once support - -With Ackers enabled, there are two kinds of At Least Once support in both Storm 0.9.x and Storm 0.10.x. - -1. spout will replay messages on message loss as long as spout is alive -2. If `KafkaSpout` is used, messages could be replayed from Kafka even if the spout crashes. - -Gearpump supports the second for both Storm versions. - -### Security support - -Storm 0.10.x adds security support for following connectors - -* [storm-hdfs](https://github.com/apache/storm/blob/0.10.x-branch/external/storm-hdfs/README.md) -* [storm-hive](https://github.com/apache/storm/blob/0.10.x-branch/external/storm-hive/README.md) -* [storm-hbase](https://github.com/apache/storm/blob/0.10.x-branch/external/storm-hbase/README.md) - -That means users could access kerberos enabled HDFS, Hive and HBase with these connectors. Generally, Storm provides two approaches (please refer to above links for more information) - -1. configure nimbus to automatically get delegation tokens on behalf of the topology submitter user -2. kerberos keytabs are already distributed on worker hosts; users configure keytab path and principal - -Gearpump supports the second approach and users needs to add classpath of HDFS/Hive/HBase to `gearpump.executor.extraClasspath` in `gear.conf` on each node. For example, - - :::json - ################### - ### Executor argument configuration - ### Executor JVM can contains multiple tasks - ################### - executor { - vmargs = "-server -Xms512M -Xmx1024M -Xss1M -XX:+HeapDumpOnOutOfMemoryError -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=80 -XX:+UseParNewGC -XX:NewRatio=3 -Djava.rmi.server.hostname=localhost" - extraClasspath = "/etc/hadoop/conf" - } - - -## How to run a Storm application on Gearpump - -This section shows how to run an existing Storm jar in a local Gearpump cluster. - -1. launch a local cluster - - :::bash - bin/local - - -2. start a Gearpump Nimbus server - - Users need server's address(`nimbus.host` and `nimbus.thrift.port`) to submit topologies later. The address is written to a yaml config file set with `-output` option. - Users can provide an existing config file where only the address will be overwritten. If not provided, a new file `app.yaml` is created with the config. - - :::bash - bin/storm nimbus -output [conf <custom yaml config>] - - -3. submit Storm applications - - Users can either submit Storm applications through command line or UI. - - a. submit Storm applications through command line - - :::bash - bin/storm app -verbose -config app.yaml -jar storm-starter-${STORM_VERSION}.jar storm.starter.ExclamationTopology exclamation - - - Users are able to configure their applications through following options - - * `jar` - set the path of a Storm application jar - * `config` - submit the custom configuration file generated when launching Nimbus - - - b. submit Storm application through UI - - 1. Click on the "Create" button on the applications page on UI. - 2. Click on the "Submit Storm Application" item in the pull down menu. - 3. In the popup console, upload the Storm application jar and the configuration file generated when launching Nimbus, - and fill in `storm.starter.ExclamationTopology exclamation` as arguments. - 4. Click on the "Submit" button - - Either way, check the dashboard and you should see data flowing through your topology. - -## How is it different from running on Storm - -### Topology submission - -When a client submits a Storm topology, Gearpump launches locally a simplified version of Storm's Nimbus server `GearpumpNimbus`. `GearpumpNimbus` then translates topology to a directed acyclic graph (DAG) of Gearpump, which is submitted to Gearpump master and deployed as a Gearpump application. - - - -`GearpumpNimbus` supports the following methods - -* `submitTopology` / `submitTopologyWithOpts` -* `killTopology` / `killTopologyWithOpts` -* `getTopology` / `getUserTopology` -* `getClusterInfo` - -### Topology translation - -Here's an example of `WordCountTopology` with acker bolts (ackers) being translated into a Gearpump DAG. - - - -Gearpump creates a `StormProducer` for each Storm spout and a `StormProcessor` for each Storm bolt (except for ackers) with the same parallelism, and wires them together using the same grouping strategy (partitioning in Gearpump) as in Storm. - -At runtime, spouts and bolts are running inside `StormProducer` tasks and `StormProcessor` tasks respectively. Messages emitted by spout are passed to `StormProducer`, transferred to `StormProcessor` and passed down to bolt. Messages are serialized / de-serialized with Storm serializers. - -Storm ackers are dropped since Gearpump has a different mechanism of message tracking and flow control. - -### Task execution - -Each Storm task is executed by a dedicated thread while all Gearpump tasks of an executor share a thread pool. Generally, we can achieve better performance with a shared thread pool. It's possible, however, some tasks block and take up all the threads. In that case, we can -fall back to the Storm way by setting `gearpump.task-dispatcher` to `"gearpump.single-thread-dispatcher"` in `gear.conf`. - -### Message tracking - -Storm tracks the lineage of each message with ackers to guarantee at-least-once message delivery. Failed messages are re-sent from spout. - -Gearpump [tracks messages between a sender and receiver in an efficient way](../introduction/gearpump-internals#how-do-we-detect-message-loss). Message loss causes the whole application to replay from the [minimum timestamp of all pending messages in the system](../introduction/gearpump-internals#application-clock-and-global-clock-service). - -### Flow control - -Storm throttles flow rate at spout, which stops sending messages if the number of unacked messages exceeds `topology.max.spout.pending`. - -Gearpump has flow control between tasks such that [sender cannot flood receiver](../introduction/gearpump-internals#how-do-we-do-flow-control), which is backpressured till the source. - -### Configurations - -All Storm configurations are respected with the following priority order - - :::bash - defaults.yaml < custom file config < application config < component config - - -where - -* application config is submit from Storm application along with the topology -* component config is set in spout / bolt with `getComponentConfiguration` -* custom file config is specified with the `-config` option when submitting Storm application from command line or uploaded from UI - -## StreamCQL Support - -[StreamCQL](https://github.com/HuaweiBigData/StreamCQL) is a Continuous Query Language on RealTime Computation System open sourced by Huawei. -Since StreamCQL already supports Storm, it's straightforward to run StreamCQL over Gearpump. - -1. Install StreamCQL as in the official [README](https://github.com/HuaweiBigData/StreamCQL#install-streamcql) - -2. Launch Gearpump Nimbus Server as before - -3. Go to the installed stream-cql-binary, and change following settings in `conf/streaming-site.xml` with the output Nimbus configs in Step 2. - - :::xml - <property> - <name>streaming.storm.nimbus.host</name> - <value>${nimbus.host}</value> - </property> - <property> - <name>streaming.storm.nimbus.port</name> - <value>${nimbus.thrift.port}</value> - </property> - - -4. Open CQL client shell with `bin/cql` and execute a simple cql example - - :::sql - Streaming> CREATE INPUT STREAM s - (id INT, name STRING, type INT) - SOURCE randomgen - PROPERTIES ( timeUnit = "SECONDS", period = "1", - eventNumPerperiod = "1", isSchedule = "true" ); - - CREATE OUTPUT STREAM rs - (type INT, cc INT) - SINK consoleOutput; - - INSERT INTO STREAM rs SELECT type, COUNT(id) as cc - FROM s[RANGE 20 SECONDS BATCH] - WHERE id > 5 GROUP BY type; - - SUBMIT APPLICATION example; - - -5. Check the dashboard and you should see data flowing through a topology of 3 components. - http://git-wip-us.apache.org/repos/asf/incubator-gearpump/blob/cc0578e5/docs/docs/dev/dev-write-1st-app.md ---------------------------------------------------------------------- diff --git a/docs/docs/dev/dev-write-1st-app.md b/docs/docs/dev/dev-write-1st-app.md deleted file mode 100644 index f28a303..0000000 --- a/docs/docs/dev/dev-write-1st-app.md +++ /dev/null @@ -1,370 +0,0 @@ -We'll use [wordcount](https://github.com/apache/incubator-gearpump/tree/master/examples/streaming/wordcount/src/main/scala/org/apache/gearpump/streaming/examples/wordcount) as an example to illustrate how to write Gearpump applications. - -### Maven/Sbt Settings - -Repository and library dependencies can be found at [Maven Setting](http://gearpump.apache.org/downloads.html#maven-dependencies). - -### IDE Setup (Optional) -You can get your preferred IDE ready for Gearpump by following [this guide](dev-ide-setup). - -### Decide which language and API to use for writing -Gearpump supports two level APIs: - -1. Low level API, which is more similar to Akka programming, operating on each event. The API document can be found at [Low Level API Doc](http://gearpump.apache.org/releases/latest/api/scala/index.html#org.apache.gearpump.streaming.package). - -2. High level API (aka DSL), which is operating on streaming instead of individual event. The API document can be found at [DSL API Doc](http://gearpump.apache.org/releases/latest/api/scala/index.html#org.apache.gearpump.streaming.dsl.package). - -And both APIs have their Java version and Scala version. - -So, before you writing your first Gearpump application, you need to decide which API to use and which language to use. - -## DSL version for Wordcount - -The easiest way to write your streaming application is to write it with Gearpump DSL. -Below will demostrate how to write WordCount application via Gearpump DSL. - -#### In Scala - - :::scala - /** WordCount with High level DSL */ - object WordCount extends AkkaApp with ArgumentsParser { - - override val options: Array[(String, CLIOption[Any])] = Array.empty - - override def main(akkaConf: Config, args: Array[String]): Unit = { - val context = ClientContext(akkaConf) - val app = StreamApp("dsl", context) - val data = "This is a good start, bingo!! bingo!!" - - //count for each word and output to log - app.source(data.lines.toList, 1, "source"). - // word => (word, count) - flatMap(line => line.split("[\\s]+")).map((_, 1)). - // (word, count1), (word, count2) => (word, count1 + count2) - groupByKey().sum.log - - val appId = context.submit(app) - context.close() - } - } - - -#### In Java - - :::java - /** Java version of WordCount with high level DSL API */ - public class WordCount { - - public static void main(String[] args) throws InterruptedException { - main(ClusterConfig.defaultConfig(), args); - } - - public static void main(Config akkaConf, String[] args) throws InterruptedException { - ClientContext context = new ClientContext(akkaConf); - JavaStreamApp app = new JavaStreamApp("JavaDSL", context, UserConfig.empty()); - List<String> source = Lists.newArrayList("This is a good start, bingo!! bingo!!"); - - //create a stream from the string list. - JavaStream<String> sentence = app.source(source, 1, UserConfig.empty(), "source"); - - //tokenize the strings and create a new stream - JavaStream<String> words = sentence.flatMap(new FlatMapFunction<String, String>() { - @Override - public Iterator<String> apply(String s) { - return Lists.newArrayList(s.split("\\s+")).iterator(); - } - }, "flatMap"); - - //map each string as (string, 1) pair - JavaStream<Tuple2<String, Integer>> ones = words.map(new MapFunction<String, Tuple2<String, Integer>>() { - @Override - public Tuple2<String, Integer> apply(String s) { - return new Tuple2<String, Integer>(s, 1); - } - }, "map"); - - //group by according to string - JavaStream<Tuple2<String, Integer>> groupedOnes = ones.groupBy(new GroupByFunction<Tuple2<String, Integer>, String>() { - @Override - public String apply(Tuple2<String, Integer> tuple) { - return tuple._1(); - } - }, 1, "groupBy"); - - //for each group, make the sum - JavaStream<Tuple2<String, Integer>> wordcount = groupedOnes.reduce(new ReduceFunction<Tuple2<String, Integer>>() { - @Override - public Tuple2<String, Integer> apply(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) { - return new Tuple2<String, Integer>(t1._1(), t1._2() + t2._2()); - } - }, "reduce"); - - //output result using log - wordcount.log(); - - app.run(); - context.close(); - } - } - - -## Low level API based Wordcount - -### Define Processor(Task) class and Partitioner class - -An application is a Directed Acyclic Graph (DAG) of processors. In the wordcount example, We will firstly define two processors `Split` and `Sum`, and then weave them together. - - -#### Split processor - -In the `Split` processor, we simply split a predefined text (the content is simplified for conciseness) and send out each split word to `Sum`. - -#### In Scala - - :::scala - class Split(taskContext : TaskContext, conf: UserConfig) extends Task(taskContext, conf) { - import taskContext.output - - override def onStart(startTime : StartTime) : Unit = { - self ! Message("start") - } - - override def onNext(msg : Message) : Unit = { - Split.TEXT_TO_SPLIT.lines.foreach { line => - line.split("[\\s]+").filter(_.nonEmpty).foreach { msg => - output(new Message(msg, System.currentTimeMillis())) - } - } - self ! Message("continue", System.currentTimeMillis()) - } - } - - object Split { - val TEXT_TO_SPLIT = "some text" - } - -#### In Java - - :::java - public class Split extends Task { - - public static String TEXT = "This is a good start for java! bingo! bingo! "; - - public Split(TaskContext taskContext, UserConfig userConf) { - super(taskContext, userConf); - } - - private Long now() { - return System.currentTimeMillis(); - } - - @Override - public void onStart(StartTime startTime) { - self().tell(new Message("start", now()), self()); - } - - @Override - public void onNext(Message msg) { - - // Split the TEXT to words - String[] words = TEXT.split(" "); - for (int i = 0; i < words.length; i++) { - context.output(new Message(words[i], now())); - } - self().tell(new Message("next", now()), self()); - } - } - ``` - - -Essentially, each processor consists of two descriptions: - -1. A `Task` to define the operation. - -2. A parallelism level to define the number of tasks of this processor in parallel. - -Just like `Split`, every processor extends `Task`. The `onStart` method is called once before any message comes in; `onNext` method is called to process every incoming message. Note that Gearpump employs the message-driven model and that's why Split sends itself a message at the end of `onStart` and `onNext` to trigger next message processing. - -#### Sum Processor - -The structure of `Sum` processor looks much alike. `Sum` does not need to send messages to itself since it receives messages from `Split`. - -#### In Scala - - :::scala - class Sum (taskContext : TaskContext, conf: UserConfig) extends Task(taskContext, conf) { - private[wordcount] val map : mutable.HashMap[String, Long] = new mutable.HashMap[String, Long]() - - private[wordcount] var wordCount : Long = 0 - private var snapShotTime : Long = System.currentTimeMillis() - private var snapShotWordCount : Long = 0 - - private var scheduler : Cancellable = null - - override def onStart(startTime : StartTime) : Unit = { - scheduler = taskContext.schedule(new FiniteDuration(5, TimeUnit.SECONDS), - new FiniteDuration(5, TimeUnit.SECONDS))(reportWordCount) - } - - override def onNext(msg : Message) : Unit = { - if (null == msg) { - return - } - val current = map.getOrElse(msg.msg.asInstanceOf[String], 0L) - wordCount += 1 - map.put(msg.msg.asInstanceOf[String], current + 1) - } - - override def onStop() : Unit = { - if (scheduler != null) { - scheduler.cancel() - } - } - - def reportWordCount() : Unit = { - val current : Long = System.currentTimeMillis() - LOG.info(s"Task ${taskContext.taskId} Throughput: ${(wordCount - snapShotWordCount, (current - snapShotTime) / 1000)} (words, second)") - snapShotWordCount = wordCount - snapShotTime = current - } - } - -#### In Java - - :::java - public class Sum extends Task { - - private Logger LOG = super.LOG(); - private HashMap<String, Integer> wordCount = new HashMap<String, Integer>(); - - public Sum(TaskContext taskContext, UserConfig userConf) { - super(taskContext, userConf); - } - - @Override - public void onStart(StartTime startTime) { - //skip - } - - @Override - public void onNext(Message messagePayLoad) { - String word = (String) (messagePayLoad.msg()); - Integer current = wordCount.get(word); - if (current == null) { - current = 0; - } - Integer newCount = current + 1; - wordCount.put(word, newCount); - } - } - - -Besides counting the sum, in Scala version, we also define a scheduler to report throughput every 5 seconds. The scheduler should be cancelled when the computation completes, which could be accomplished overriding the `onStop` method. The default implementation of `onStop` is a no-op. - -#### Partitioner - -A processor could be parallelized to a list of tasks. A `Partitioner` defines how the data is shuffled among tasks of Split and Sum. Gearpump has already provided two partitioners - -* `HashPartitioner`: partitions data based on the message's hashcode -* `ShufflePartitioner`: partitions data in a round-robin way. - -You could define your own partitioner by extending the `Partitioner` trait/interface and overriding the `getPartition` method. - - - :::scala - trait Partitioner extends Serializable { - def getPartition(msg : Message, partitionNum : Int) : Int - } - -### Wrap up as an application - -Now, we are able to write our application class, weaving the above components together. - -The application class extends `App` and `ArgumentsParser which make it easier to parse arguments and run main functions. - -#### In Scala - - :::scala - object WordCount extends App with ArgumentsParser { - private val LOG: Logger = LogUtil.getLogger(getClass) - val RUN_FOR_EVER = -1 - - override val options: Array[(String, CLIOption[Any])] = Array( - "split" -> CLIOption[Int]("<how many split tasks>", required = false, defaultValue = Some(1)), - "sum" -> CLIOption[Int]("<how many sum tasks>", required = false, defaultValue = Some(1)) - ) - - def application(config: ParseResult) : StreamApplication = { - val splitNum = config.getInt("split") - val sumNum = config.getInt("sum") - val partitioner = new HashPartitioner() - val split = Processor[Split](splitNum) - val sum = Processor[Sum](sumNum) - val app = StreamApplication("wordCount", Graph[Processor[_ <: Task], Partitioner](split ~ partitioner ~> sum), UserConfig.empty) - app - } - - val config = parse(args) - val context = ClientContext() - val appId = context.submit(application(config)) - context.close() - } - - - -We override `options` value and define an array of command line arguments to parse. We want application users to pass in masters' hosts and ports, the parallelism of split and sum tasks, and how long to run the example. We also specify whether an option is `required` and provide `defaultValue` for some arguments. - -#### In Java - - :::java - - /** Java version of WordCount with Processor Graph API */ - public class WordCount { - - public static void main(String[] args) throws InterruptedException { - main(ClusterConfig.defaultConfig(), args); - } - - public static void main(Config akkaConf, String[] args) throws InterruptedException { - - // For split task, we config to create two tasks - int splitTaskNumber = 2; - Processor split = new Processor(Split.class).withParallelism(splitTaskNumber); - - // For sum task, we have two summer. - int sumTaskNumber = 2; - Processor sum = new Processor(Sum.class).withParallelism(sumTaskNumber); - - // construct the graph - Graph graph = new Graph(); - graph.addVertex(split); - graph.addVertex(sum); - - Partitioner partitioner = new HashPartitioner(); - graph.addEdge(split, partitioner, sum); - - UserConfig conf = UserConfig.empty(); - StreamApplication app = new StreamApplication("wordcountJava", conf, graph); - - // create master client - // It will read the master settings under gearpump.cluster.masters - ClientContext masterClient = new ClientContext(akkaConf); - - masterClient.submit(app); - - masterClient.close(); - } - } - - -## Submit application - -After all these, you need to package everything into a uber jar and submit the jar to Gearpump Cluster. 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powerful at streaming water. - -### Gearpump Technical Highlights -Gearpump's feature set includes: - -* Extremely high performance -* Low latency -* Configurable message delivery guarantee (at least once, exactly once). -* Highly extensible -* Dynamic DAG -* Storm compatibility -* Samoa compatibility -* Both high level and low level API - -### Gearpump Performance -Per initial benchmarks we are able to process 18 million messages/second (100 bytes per message) with a 8ms latency on a 4-node cluster. - - - -### Gearpump and Akka -Gearpump is a 100% Akka based platform. We model big data streaming within the Akka actor hierarchy. - http://git-wip-us.apache.org/repos/asf/incubator-gearpump/blob/cc0578e5/docs/docs/introduction/basic-concepts.md ---------------------------------------------------------------------- diff --git a/docs/docs/introduction/basic-concepts.md b/docs/docs/introduction/basic-concepts.md deleted file mode 100644 index a4c49b7..0000000 --- a/docs/docs/introduction/basic-concepts.md +++ /dev/null @@ -1,46 +0,0 @@ -### System timestamp and Application timestamp - -System timestamp denotes the time of backend cluster system. Application timestamp denotes the time at which message is generated. For example, for IoT edge device, the timestamp at which field sensor device creates a message is type of application timestamp, while the timestamp at which that message get received by the backend is type of system time. - -### Master, and Worker - -Gearpump follow master slave architecture. Every cluster contains one or more Master node, and several worker nodes. Worker node is responsible to manage local resources on single machine, and Master node is responsible to manage global resources of the whole cluster. - - - -### Application - -Application is what we want to parallel and run on the cluster. There are different application types, for example MapReduce application and streaming application are different application types. Gearpump natively supports Streaming Application types, it also contains several templates to help user to create custom application types, like distributedShell. - -### AppMaster and Executor - -In runtime, every application instance is represented by a single AppMaster and a list of Executors. AppMaster represents the command and controls center of the Application instance. It communicates with user, master, worker, and executor to get the job done. Each executor is a parallel unit for distributed application. Typically AppMaster and Executor will be started as JVM processes on worker nodes. - -### Application Submission Flow - -When user submits an application to Master, Master will first find an available worker to start the AppMaster. After AppMaster is started, AppMaster will request Master for more resources (worker) to start executors. The Executor now is only an empty container. After the executors are started, the AppMaster will then distribute real computation tasks to the executor and run them in parallel way. - -To submit an application, a Gearpump client specifies a computation defined within a DAG and submits this to an active master. The SubmitApplication message is sent to the Master who then forwards this to an AppManager. - - -Figure: User Submit Application - -The AppManager locates an available worker and launches an AppMaster in a sub-process JVM of the worker. The AppMaster will then negotiate with the Master for Resource allocation in order to distribute the DAG as defined within the Application. The allocated workers will then launch Executors (new JVMs). - - -Figure: Launch Executors and Tasks - -### Streaming Topology, Processor, and Task - -For streaming application type, each application contains a topology, which is a DAG (directed acyclic graph) to describe the data flow. Each node in the DAG is a processor. For example, for word count it contains two processors, Split and Sum. The Split processor splits a line to a list of words, and then the Sum processor summarize the frequency of each word. -An application is a DAG of processors. Each processor handles messages. - - -Figure: Processor DAG - -### Streaming Task and Partitioner - -For streaming application type, Task is the minimum unit of parallelism. In runtime, each Processor is paralleled to a list of tasks, with different tasks running in different executor. You can define Partitioner to denote the data shuffling rule between upstream processor tasks and downstream processor tasks. - - -Figure: Task Data Shuffling http://git-wip-us.apache.org/repos/asf/incubator-gearpump/blob/cc0578e5/docs/docs/introduction/commandline.md ---------------------------------------------------------------------- diff --git a/docs/docs/introduction/commandline.md b/docs/docs/introduction/commandline.md deleted file mode 100644 index 3dfe885..0000000 --- a/docs/docs/introduction/commandline.md +++ /dev/null @@ -1,84 +0,0 @@ -The commands can be found at: "bin" folder of Gearpump binary. - -**NOTE:** on MS Windows platform, please use window shell gear.bat script instead. bash script doesn't work well in cygwin/mingw. - -### Creating an uber-jar - -If you use Maven you can have a look [here](https://maven.apache.org/plugins/maven-shade-plugin/) whereas SBT users may find [this](https://github.com/sbt/sbt-assembly) useful. - -### Submit an new application - -You can use the command `gear` under the bin directory to submit, query and terminate an application: - - :::bash - gear app [-namePrefix <application name prefix>] [-executors <number of executors to launch>] [-conf <custom gearpump config file>] -jar xx.jar MainClass <arg1> <arg2> ... - - -### List all running applications -To list all running applications: - - :::bash - gear info [-conf <custom gearpump config file>] - - -### Kill a running application -To kill an application: - - :::bash - gear kill -appid <application id> [-conf <custom gearpump config file>] - - -### Submit a storm application to Gearpump Cluster -For example, to submit a storm application jar: - - :::bash - storm -verbose -config storm.yaml -jar storm-starter-${STORM_VERSION}.jar storm.starter.ExclamationTopology exclamation - - -[Storm Compatibility Guide](../dev/dev-storm) - -### Start Gearpump Cluster on YARN -To start a Gearpump Cluster on YARN, you can: - - :::bash - yarnclient launch -package /usr/lib/gearpump/gearpump-{{SCALA_BINARY_VERSION}}-{{GEARPUMP_VERSION}}.zip - -`/usr/lib/gearpump/gearpump-{{SCALA_BINARY_VERSION}}-{{GEARPUMP_VERSION}}.zip` should be available on HDFS. - -Please check [YARN Deployment Guide](../deployment/deployment-yarn) for more information. - -### Start a local cluster -Masters and workers will be started in one machine: - - :::bash - local - - -Check [Deployment Guide for Local Cluster](../deployment/deployment-local) for more information. - -### Start master daemons - - :::bash - master -ip <Ip address> -port <port where this master is hooking> - - -Please check [Deployment for Standalone mode](../deployment/deployment-standalone) for more information. - -### Start worker daemons - - :::bash - worker - - -Please check [Deployment for Standalone mode](../deployment/deployment-standalone) for more information. - -### Start UI server - -To start UI server, you can: - - :::bash - services [-master <host:port>] - - -The default username and password is "admin:admin", you can check -[UI Authentication](../deployment/deployment-ui-authentication) to find how to manage users. \ No newline at end of file http://git-wip-us.apache.org/repos/asf/incubator-gearpump/blob/cc0578e5/docs/docs/introduction/features.md ---------------------------------------------------------------------- diff --git a/docs/docs/introduction/features.md b/docs/docs/introduction/features.md deleted file mode 100644 index 4fcb66b..0000000 --- a/docs/docs/introduction/features.md +++ /dev/null @@ -1,67 +0,0 @@ -### Technical highlights of Gearpump - -Gearpump is a high performance, flexible, fault-tolerant, and responsive streaming platform with a lot of nice features, its technical highlights include: - -#### Actors everywhere - -The Actor model is a concurrency model proposed by Carl Hewitt at 1973. The Actor model is like a micro-service which is cohesive in the inside and isolated from other outside actors. Actors are the cornerstone of Gearpump, they provide facilities to do message passing, error handling, liveliness monitoring. Gearpump uses Actors everywhere; every entity within the cluster that can be treated as a service. - - - -#### Exactly once Message Processing - -Exactly once is defined as: the effect of a message will be calculated only once in the persisted state and computation errors in the history will not be propagated to future computations. - - - -#### Topology DAG DSL - -User can submit to Gearpump a computation DAG, which contains a list of nodes and edges, and each node can be parallelized to a set of tasks. Gearpump will then schedule and distribute different tasks in the DAG to different machines automatically. Each task will be started as an actor, which is long running micro-service. - - - -#### Flow control - -Gearpump has built-in support for flow control. For all message passing between different tasks, the framework will assure the upstream tasks will not flood the downstream tasks. - - -#### No inherent end to end latency - -Gearpump is a message level streaming engine, which means every task in the DAG will process messages immediately upon receiving, and deliver messages to downstream immediately without waiting. Gearpump doesn't do batching when data sourcing. - -#### High Performance message passing - -By implementing smart batching strategies, Gearpump is extremely effective in transferring small messages. In one test of 4 machines, the whole cluster throughput can reach 18 million messages per second, with message size of 100 bytes. - - -#### High availability, No single point of failure - -Gearpump has a careful design for high availability. We have considered message loss, worker machine crash, application crash, master crash, brain-split, and have made sure Gearpump recovers when these errors may occur. When there is message loss, the lost message will be replayed; when there is a worker machine crash or application crash, the related computation tasks will be rescheduled on new machines. For master high availability, several master nodes will form a Akka cluster, and CRDTs (conflict free data types) are used to exchange the state, so as long as there is still a quorum, the master will stay functional. When one master node fails, other master nodes in the cluster will take over and state will be recovered. - - - -#### Dynamic Computation DAG - -Gearpump provides a feature which allows the user to dynamically add, remove, or replace a sub graph at runtime, without the need to restart the whole computation topology. - - - -#### Able to handle out of order messages - -For a window operation like moving average on a sliding window, it is important to make sure we have received all messages in that time window so that we can get an accurate result, but how do we handle stranglers or late arriving messages? Gearpump solves this problem by tracking the low watermark of timestamp of all messages, so it knows whether we've received all the messages in the time window or not. - - - -#### Customizable platform - -Different applications have different requirements related to performance metrics, some may want higher throughput, some may require strong eventual data consistency; and different applications have different resource requirements profiles, some may demand high CPU performance, some may require data locality. Gearpump meets these requirements by allowing the user to arbitrate between different performance metrics and define customized resource scheduling strategies. - -#### Built-in Dashboard UI - -Gearpump has a built-in dashboard UI to manage the cluster and visualize the applications. The UI uses REST calls to connect with backend, so it is easy to embed the UI within other dashboards. - - - -#### Data connectors for Kafka and HDFS - -Gearpump has built-in data connectors for Kafka and HDFS. For the Kafka connector, we support message replay from a specified timestamp.
