This page highlights major changes in each version and upgrade tools.

How to Upgrade

Upgrade to 0.14.0

This release adds Elasticsearch 6 support. See pull request for details. Consequently, you must reindex your data.

1. Access your old cluster to check existing indices

$ curl -XGET 'http://localhost:9200/_cat/indices?v'
health status index     uuid                   pri rep docs.count docs.deleted store.size pri.store.size
yellow open   pio_event 6BAPz-DfQ2e9bICdVRr03g   5   1       1501            0    321.3kb        321.3kb
yellow open   pio_meta  oxDMU1mGRn-vnXtAjmifSw   5   1          4            0     32.4kb         32.4kb

$ curl -XGET "http://localhost:9200/pio_meta/_search" -d'
{
  "aggs": {
    "typesAgg": {
      "terms": {
        "field": "_type",
        "size": 200
      }
    }
  },
  "size": 0
}'
{"took":3,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":4,"max_score":0.0,"hits":[]},"aggregations":{"typesAgg":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{"key":"accesskeys","doc_count":1},{"key":"apps","doc_count":1},{"key":"engine_instances","doc_count":1},{"key":"sequences","doc_count":1}]}}}

$ curl -XGET "http://localhost:9200/pio_event/_search" -d'
{
  "aggs": {
    "typesAgg": {
      "terms": {
        "field": "_type",
        "size": 200
      }
    }
  },
  "size": 0
}'
{"took":2,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":1501,"max_score":0.0,"hits":[]},"aggregations":{"typesAgg":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{"key":"1","doc_count":1501}]}}}

2. (Optional) Settings for new indices

If you want to add specific settings associated with each index, we would recommend defining Index Templates.

For example,

$ curl -H "Content-Type: application/json" -XPUT "http://localhost:9600/_template/pio_meta" -d'
{
  "index_patterns": ["pio_meta_*"],
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  }
}'
$ curl -H "Content-Type: application/json" -XPUT "http://localhost:9600/_template/pio_event" -d'
{
  "index_patterns": ["pio_event_*"],
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  }
}'

3. Reindex

According to the following conversion table, you run the reindex every index that you need to migrate to your new cluster.

For example,

$ curl -H "Content-Type: application/json" -XPOST "http://localhost:9600/_reindex" -d'
{
  "source": {
    "remote": {
      "host": "http://localhost:9200"
    },
    "index": "pio_meta",
    "type": "accesskeys"
  },
  "dest": {
    "index": "pio_meta_accesskeys"
  }
}'

Upgrade to 0.12.0

In 0.12.0, Elasticsearch 5.x client has been reimplemented as a singleton. Engine templates directly using Elasticsearch 5.x StorageClient require update for compatibility. See [pull request] (https://github.com/apache/predictionio/pull/421) for details.

Upgrade to 0.11.0

Starting from 0.11.0, PredictionIO no longer bundles any JDBC drivers in the binary assembly. If your setup is using a JDBC backend and you run into storage connection errors after an upgrade, please manually install the JDBC driver. If you use PostgreSQL, you can find instructions here.

Upgrade to 0.9.2

The Spark dependency has been upgraded to version 1.3.0. All engines must be rebuilt against it in order to work.

Open and edit build.sbt of your engine, and look for these two lines:

"org.apache.spark" %% "spark-core"    % "1.2.0" % "provided"

"org.apache.spark" %% "spark-mllib"   % "1.2.0" % "provided"

Change 1.2.0 to 1.3.0, and do a clean rebuild by pio build --clean. Your engine should now work with the latest Apache Spark.

New PEventStore and LEventStore API

In addition, new PEventStore and LEventStore API are introduced so that appName can be used as parameters in engine.json to access Event Store.

NOTE: The following changes are not required for using 0.9.2 but it's recommended to upgrade your engine code as described below because the old API will be deprecated.

1. In DataSource.scala:

• remove this line of code:

  scalaCopy

  import org.apache.predictionio.data.storage.Storage
  

  and replace it by

  scalaCopy

  import org.apache.predictionio.data.store.PEventStore
  

• Change appId: Int to appName: String in DataSourceParams

  For example,

  scalaCopy

  case class DataSourceParams(appName: String) extends Params
  

• remove this line of code: val eventsDb = Storage.getPEvents()

• locate where eventsDb.aggregateProperties() is used, change it to PEventStore.aggregateProperties():

  For example,

  scalaCopy

  
    val usersRDD: RDD[(String, User)] = PEventStore.aggregateProperties( // CHANGED
      appName = dsp.appName, // CHANGED: use appName
      entityType = "user"
    )(sc).map { ... }
  
  

• locate where eventsDb.find() is used, change it to PEventStore.find()

  For example,

  scalaCopy

  
    val viewEventsRDD: RDD[ViewEvent] = PEventStore.find( // CHANGED
      appName = dsp.appName, // CHANGED: use appName
      entityType = Some("user"),
      ...
  
  

2. In XXXAlgorithm.scala:

If Storage.getLEvents() is also used in Algorithm (such as ALSAlgorithm of E-Commerce Recommendation template), you also need to do following:

NOTE: If org.apache.predictionio.data.storage.Storage is not used at all (such as Recommendation, Similar Product, Classification, Lead Scoring, Product Ranking template), there is no need to change Algorithm and can go to the later engine.json section.

• remove import org.apache.predictionio.data.storage.Storage and replace it by import org.apache.predictionio.data.store.LEventStore

• change appId to appName in the XXXAlgorithmParams class.

• remove this line of code: @transient lazy val lEventsDb = Storage.getLEvents()

• locate where lEventsDb.findSingleEntity() is used, change it to LEventStore.findByEntity():

  For example, change following code

  scalaCopy

    ...
    val seenEvents: Iterator[Event] = lEventsDb.findSingleEntity(
      appId = ap.appId,
      entityType = "user",
      entityId = query.user,
      eventNames = Some(ap.seenEvents),
      targetEntityType = Some(Some("item")),
      // set time limit to avoid super long DB access
      timeout = Duration(200, "millis")
    ) match {
      case Right(x) => x
      case Left(e) => {
        logger.error(s"Error when read seen events: ${e}")
        Iterator[Event]()
      }
    }
  

  to

  scalaCopy

    val seenEvents: Iterator[Event] = try { // CHANGED: try catch block is used
      LEventStore.findByEntity( // CHANGED: new API
        appName = ap.appName, // CHANGED: use appName
        entityType = "user",
        entityId = query.user,
        eventNames = Some(ap.seenEvents),
        targetEntityType = Some(Some("item")),
        // set time limit to avoid super long DB access
        timeout = Duration(200, "millis")
      )
    } catch { // CHANGED: try catch block is used
      case e: scala.concurrent.TimeoutException =>
        logger.error(s"Timeout when read seen events." +
          s" Empty list is used. ${e}")
        Iterator[Event]()
      case e: Exception =>
        logger.error(s"Error when read seen events: ${e}")
        throw e
    }
  

  If you are using E-Commerce Recommendation template, please refer to the latest version for other updates related to LEventStore.findByEntity()

3. In engine.json:

locate where appId is used, change it to appName and specify the name of the app instead.

For example:

  ...

  "datasource": {
    "params" : {
      "appName": "MyAppName"
    }
  },

Note that other components such as algorithms may also have appId param (e.g. E-Commerce Recommendation template). Remember to change it to appName as well.

That's it! You can re-biuld your engine to try it out!

Upgrade to 0.9.0

0.9.0 has the following new changes:

• The signature of P2LAlgorithm and PAlgorithm's train() method is changed from

  scalaCopy

    def train(pd: PD): M
  

  to

  scalaCopy

    def train(sc: SparkContext, pd: PD): M
  

  which allows you to access SparkContext inside train() with this new parameter sc.

• A new SBT build plugin (pio-build) is added for engine template

WARNING: If you have existing engine templates running with previous version of PredictionIO, you need to either download the latest templates which are compatible with 0.9.0, or follow the instructions below to modify them.

Follow instructions below to modify existing engine templates to be compatible with PredictionIO 0.9.0:

1. Add a new parameter sc: SparkContext in the signature of train() method of algorithm in the templates.

   For example, in Recommendation engine template, you will find the following train() function in ALSAlgorithm.scala

   scalaCopy

   class ALSAlgorithm(val ap: ALSAlgorithmParams)
     extends P2LAlgorithm[PreparedData, ALSModel, Query, PredictedResult] {
   
     ...
   
     def train(data: PreparedData): ALSModel = ...
   
     ...
   }
   

   Simply add the new parameter sc: SparkContext, to train() function signature:

   scalaCopy

   class ALSAlgorithm(val ap: ALSAlgorithmParams)
     extends P2LAlgorithm[PreparedData, ALSModel, Query, PredictedResult] {
   
     ...
   
     def train(sc: SparkContext, data: PreparedData): ALSModel = ...
   
     ...
   }
   

   You need to add the following import for your algorithm as well if it is not there:

   scalaCopy

   import org.apache.spark.SparkContext
   

2. Modify the file build.sbt in your template directory to use pioVersion.value as the version of org.apache.predictionio.core dependency:

   Under your template's root directory, you should see a file build.sbt which has the following content:

   Copy

   libraryDependencies ++= Seq(
     "org.apache.predictionio"    %% "core"          % "0.8.6" % "provided",
     "org.apache.spark" %% "spark-core"    % "1.2.0" % "provided",
     "org.apache.spark" %% "spark-mllib"   % "1.2.0" % "provided")
   

   Change the version of "org.apache.predictionio" && "core" to pioVersion.value:

   Copy

   libraryDependencies ++= Seq(
     "org.apache.predictionio"    %% "core"          % pioVersion.value % "provided",
     "org.apache.spark" %% "spark-core"    % "1.2.0" % "provided",
     "org.apache.spark" %% "spark-mllib"   % "1.2.0" % "provided")
   

3. Create a new file pio-build.sbt in template's project/ directory with the following content:

   Copy

   addSbtPlugin("org.apache.predictionio" % "pio-build" % "0.9.0")
   

   Then, you should see the following two files in the project/ directory:

   Copy

   your_template_directory$ ls project/
   assembly.sbt  pio-build.sbt
   

4. Create a new file template.json file in the engine template's root directory with the following content:

   Copy

   {"pio": {"version": { "min": "0.9.0" }}}
   

   This is to specify the minium PredictionIO version which the engine can run with.

5. Lastly, you can add /pio.sbt into your engine template's .gitignore. pio.sbt is automatically generated by pio build.

That's it! Now you can run pio build, pio train and pio deploy with PredictionIO 0.9.0 in the same way as before!

##Upgrade to 0.8.4

engine.json has slightly changed its format in 0.8.4 in order to make engine more flexible. If you are upgrading to 0.8.4, engine.json needs to have the params field for datasource, preparator, and serving. Here is the sample engine.json from templates/scala-parallel-recommendation-custom-preparator that demonstrate the change for datasource (line 7).

In 0.8.3
{
  "id": "default",
  "description": "Default settings",
  "engineFactory": "org.template.recommendation.RecommendationEngine",
  "datasource": {
    "appId": 1
  },
  "algorithms": [
    {
      "name": "als",
      "params": {
        "rank": 10,
        "numIterations": 20,
        "lambda": 0.01
      }
    }
  ]
}

In 0.8.4
{
  "id": "default",
  "description": "Default settings",
  "engineFactory": "org.template.recommendation.RecommendationEngine",
  "datasource": {
    "params" : {
      "appId": 1
    }
  },
  "algorithms": [
    {
      "name": "als",
      "params": {
        "rank": 10,
        "numIterations": 20,
        "lambda": 0.01
      }
    }
  ]

##Upgrade from 0.8.2 to 0.8.3

0.8.3 disallows entity types pio_user and pio_item. These types are used by default for most SDKs. They are deprecated in 0.8.3, and SDKs helper functions have been updated to use user and item instead.

If you are upgrading to 0.8.3, you can follow these steps to migrate your data.

1. Create a new app

$ pio app new <my app name>

Please take note of the <new app id> generated for the new app.

2. Run the upgrade command

$ pio upgrade 0.8.2 0.8.3 <old app id> <new app id>

It will run a script that creates a new app with the new app id and migreate the data to the new app.

3. Update engine.json to use the new app id. Engine.json is located under your engine project directory.

  "datasource": {
    "appId": <new app id>
  },

Schema Changes in 0.8.2

0.8.2 contains HBase and Elasticsearch schema changes from previous versions. If you are upgrading from a pre-0.8.2 version, you need to first clear HBase and ElasticSearch. These will clear out all data in Elasticsearch and HBase. Please be extra cautious.

DANGER: ALL EXISTING DATA WILL BE LOST!

Clearing Elasticsearch

With Elasticsearch running, do

$ curl -X DELETE http://localhost:9200/_all

For details see http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-delete-index.html.

Clearing HBase

$ $HBASE_HOME/bin/hbase shell
...
> disable_all 'predictionio.*'
...
> drop_all 'predictionio.*'
...

For details see http://wiki.apache.org/hadoop/Hbase/Shell.

Experimental upgrade tool (Upgrade HBase schema from 0.8.0/0.8.1 to 0.8.2)

Create an app to store the data

$ bin/pio app new <my app>

Replace by the returned app ID: ( is the original app ID used in 0.8.0/0.8.2.)

$ set -a
$ source conf/pio-env.sh
$ set +a
$ sbt/sbt "data/run-main org.apache.predictionio.data.storage.hbase.upgrade.Upgrade <from app ID>" "<to app ID>"