There are many methods for getting data into Fusion, depending on the type of data:

Your searchable content, signals, and logs are all ingested by indexing them in Solr.
connectors, machine learning models, Upload a JDBC Driver to Fusion Server, analytics catalogs, and Banana dashboards can be uploaded to the blob store.
The Parallel Bulk Loader (PBL) can perform distributed reads on any data source that implements the SparkSQL (2.2.1 or later) Data Sources API.
In some cases, you might find that it’s best to use other ingestion methods, such as Import Data with Hive, Import Data with Pig, or pushing data to a Import Data with the REST API endpoint.

Upload a JDBC Driver to Fusion Server

The JDBC connector fetches documents from a relational database via SQL queries. Under the hood, this connector implements the Solr DataImportHandler (DIH) plugin.Fusion stores JDBC drivers in the blob store. You can upload a driver using the Fusion UI or the Blob Store API.

The JDBC V1 connector is supported in Fusion releases 5.3 and earlier.
The JDBC V2 connector is supported in Fusion releases 5.4 and later.

How to upload a JDBC driver using the Fusion UI

In the Fusion UI, navigate to System > Blobs.
Click Add.
Select JDBC Driver. The “New ‘JDBC Driver’ Upload” panel appears.
Click Choose File and select the .jar file from your file system.
Click Upload. The new driver’s blob manifest appears.

From this screen you can also delete or replace the driver.

How to install a JDBC driver using the API

Upload the JAR file to Fusion’s blob store using the /blobs/{id} endpoint. Specify an arbitrary blob ID, and a resourceType value of plugin:connector, as in this example:

curl -u USERNAME:PASSWORD -H "content-type:application/java-archive" -H "content-length:707261" -X PUT --data-binary @postgresql-42.0.0.jar http://localhost:8764/api/blobs/mydriver?resourceType=driver:jdbc

Success response:

{
  "name" : "mydriver",
  "contentType" : "application/java-archive",
  "size" : 707261,
  "modifiedTime" : "2017-06-09T19:00:48.919Z",
  "version" : 0,
  "md5" : "c67163ca764bfe632f28229c142131b5",
  "metadata" : {
    "subtype" : "driver:jdbc",
    "drivers" : "org.postgresql.Driver",
    "resourceType" : "driver:jdbc"
  }
}

Fusion automatically publishes the event to the cluster, and the listeners perform the driver installation process on each node.

If the blob ID is identical to an existing one, the old driver will be uninstalled and the new driver will installed in its place. To get the list of existing blob IDs, run: curl -u USERNAME:PASSWORD https://FUSION_HOST:FUSION_PORT/api/blobs

To verify the uploaded driver, run:

curl -u USERNAME:PASSWORD https://FUSION_HOST:FUSION_PORT/api/blobs/BLOB_ID/manifest

Where the BLOB_ID is the name specified during upload, such as “mydriver” above. A success response looks like this:

{
  "name" : "mydriver",
  "contentType" : "application/java-archive",
  "size" : 707261,
  "modifiedTime" : "2017-06-09T19:05:17.897Z",
  "version" : 1569755095787110400,
  "md5" : "c67163ca764bfe632f28229c142131b5",
  "metadata" : {
    "subtype" : "driver:jdbc",
    "drivers" : "org.postgresql.Driver",
    "resourceType" : "driver:jdbc"
  }
}

How to manually upload a JDBC V1 driver for Fusion 4.x.x releases

For Fusion 4.x.x release levels, it is recommended that you upload the JDBC driver jar files manually. This prevents some issues with dynamically loading the driver causing errors, particularly with Oracle and Microsoft SQL drivers.The following steps specify how to upload a JDBC driver JAR file to Fusion manually. The example uses the Microsoft SQL Server JDBC driver mssql-jdbc-6.6.2-jre8.jar file.

Delete the JDBC V1 driver uploaded using the Blob store upload window.
Save the JDBC V1 driver JAR file to the apps/lib folder. For example: apps\libs\mssql-jdbc-6.2.2.jre8.jar
Open the apps/jetty/connectors-classic/webapps/connectors-extra-classpath.txt file and add the following line: apps/libs/mssql-jdbc-6.2.2.jre8.jar
Restart connectors-classic.
Specify the JDBC driver class name in the Manually uploaded JDBC Driver Name field.
Enter com.microsoft.sqlserver.jdbc.SQLServerDriver in the JDBC Driver Name field.

Import Data with Hive

Fusion ships with a Serializer/Deserializer (SerDe) for Hive, included in the distribution as lucidworks-hive-serde-v2.2.6.jar in $FUSION_HOME/apps/connectors/resources/lucid.hadoop/jobs.

For Fusion 4.1.x and 4.2.x, the preferred method of importing data with Hive is to use the Parallel Bulk Loader. The import procedure does not apply to Fusion 5.x.

Prerequisites

This section covers prerequisites and background knowledge needed to help you understand the structure of this document and how the Fusion installation process works with Kubernetes.

Release Name and Namespace

Before installing Fusion, you need to choose a https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/Kubernetes namespace to install Fusion into. Think of a K8s namespace as a virtual cluster within a physical cluster. You can install multiple instances of Fusion in the same cluster in separate namespaces. However, please do not install more than one Fusion release in the same namespace.

All Fusion services must run in the same namespace, i.e. you should not try to split a Fusion cluster across multiple namespaces.__

Use a short name for the namespace, containing only letters, digits, or dashes (no dots or underscores). The setup scripts in this repo use the namespace for the Helm release name by default.

Install Helm

Helm is a package manager for Kubernetes that helps you install and manage applications on your Kubernetes cluster. Regardless of which Kubernetes platform you’re using, you need to install helm as it is required to install Fusion for any K8s platform. On MacOS, you can do:

brew install kubernetes-helm

If you already have helm installed, make sure you’re using the latest version:

brew upgrade kubernetes-helm

For other OS, please refer to the Helm installation docs: https://helm.sh/docs/using_helm/The Fusion helm chart requires that helm is greater than version 3.0.0; check your Helm version by running helm version --short.

Helm User Permissions

If you require that fusion is installed by a user with minimal permissions, instead of an admin user, then the role and cluster role that will have to be assigned to the user within the namespace that you wish to install fusion in are documented in the install-roles directory.

When working with Kubernetes on the command-line, it’s useful to create a shell alias for kubectl, e.g.:

alias k=kubectl

To use these role in a cluster, as an admin user first create the namespace that you wish to install fusion into:

k create namespace fusion-namespace

Apply the role.yaml and cluster-role.yaml files to that namespace

k apply -f cluster-role.yaml
k config set-context --current --namespace=$NAMESPACE
k apply -f role.yaml

Then bind the rolebinding and clusterolebinding to the install user:

k create --namespace fusion-namespace rolebinding fusion-install-rolebinding --role fusion-installer --user <install_user>
k create clusterrolebinding fusion-install-rolebinding --clusterrole fusion-installer --user <install_user>

You will then be able to run the helm install command as the <install_user>

Clone fusion-cloud-native from Github

You should clone this repo from github as you’ll need to run the scripts on your local workstation:

git clone https://github.com/lucidworks/fusion-cloud-native.git

You should get into the habit of pulling this repo for the latest changes before performing any maintenance operations on your Fusion cluster to ensure you have the latest updates to the scripts.

cd fusion-cloud-native
git pull

Cloning the github repo is preferred so that you can pull in updates to the scripts, but if you are not a git user, then you can download the project: https://github.com/lucidworks/fusion-cloud-native/archive/master.zip. Once downloaded, extract the zip and cd into the fusion-cloud-native-master directory.

Prerequisites

This section covers prerequisites and background knowledge needed to help you understand the structure of this document and how the Fusion installation process works with Kubernetes.

Release Name and Namespace

All Fusion services must run in the same namespace, i.e. you should not try to split a Fusion cluster across multiple namespaces.__

Use a short name for the namespace, containing only letters, digits, or dashes (no dots or underscores). The setup scripts in this repo use the namespace for the Helm release name by default.

Install Helm

brew install kubernetes-helm

If you already have helm installed, make sure you’re using the latest version:

brew upgrade kubernetes-helm

Helm User Permissions

When working with Kubernetes on the command-line, it’s useful to create a shell alias for kubectl, e.g.:

alias k=kubectl

To use these role in a cluster, as an admin user first create the namespace that you wish to install fusion into:

k create namespace fusion-namespace

Apply the role.yaml and cluster-role.yaml files to that namespace

k apply -f cluster-role.yaml
k config set-context --current --namespace=$NAMESPACE
k apply -f role.yaml

Then bind the rolebinding and clusterolebinding to the install user:

k create --namespace fusion-namespace rolebinding fusion-install-rolebinding --role fusion-installer --user <install_user>
k create clusterrolebinding fusion-install-rolebinding --clusterrole fusion-installer --user <install_user>

You will then be able to run the helm install command as the <install_user>

Clone fusion-cloud-native from Github

You should clone this repo from github as you’ll need to run the scripts on your local workstation:

git clone https://github.com/lucidworks/fusion-cloud-native.git

You should get into the habit of pulling this repo for the latest changes before performing any maintenance operations on your Fusion cluster to ensure you have the latest updates to the scripts.

cd fusion-cloud-native
git pull

Hive SerDe

This project includes tools to build a Hive SerDe to index Hive tables to Solr.

Features

Index Hive table data to Solr.
Read Solr index data to a Hive table.
Kerberos support for securing communication between Hive and Solr.
As of v2.2.4 of the SerDe, integration with http://lucidworks.com/products is supported. *Fusion’s index pipelines can be used to index data to Fusion. *Fusion’s query pipelines can be used to query Fusion’s Solr instance for data to insert into a Hive table.

This version of hive-solr supports Hive 3.0.0. For support for Hive 1.x, see the hive_1x branch.

hive-solr should only be used with Solr 5.0 and higher.

Build the SerDe Jar

This project has a dependency on the solr-hadoop-common submodule (contained in a separate GitHub repository, https://github.com/lucidworks/solr-hadoop-common). This submodule must be initialized before building the SerDe .jar.

You must use Java 8 or higher to build the .jar.

To initialize the submodule, pull this repo, then:

git submodule init

Once the submodule has been initialized, the command git submodule update will fetch all the data from that project and check out the appropriate commit listed in the superproject. You must initialize and update the submodule before attempting to build the SerDe jar.

If a build is happening from a branch, make sure that solr-hadoop-common is pointing to the correct SHA. (See https://github.com/blog/2104-working-with-submodules for more details.)
```
hive-solr $ git checkout <branch-name>
hive-solr $ cd solr-hadoop-common
hive-solr/solr-hadoop-common $ git checkout <SHA>
hive-solr/solr-hadoop-common $ cd ..
```

The build uses Gradle. However, you do not need to have Gradle already installed before attempting to build.To build the jar files, run this command:

./gradlew clean shadowJar --info

This will build a single .jar file, solr-hive-serde/build/libs/{packageUser}-hive-serde-{connectorVersion}.jar, which can be used with Hive v3.0. Other Hive versions (such as v2.x) may work with this jar, but have not been tested.

Troubleshooting Clone Issues

If GitHub and SSH are not configured the following exception will be thrown:

    Cloning into 'solr-hadoop-common'...
    Permission denied (publickey).
    fatal: Could not read from remote repository.

    Please make sure you have the correct access rights
    and the repository exists.
    fatal: clone of 'git@github.com:LucidWorks/solr-hadoop-common.git' into submodule path 'solr-hadoop-common' failed

To fix this error, use the https://help.github.com/articles/generating-an-ssh-key/ tutorial.

Add the SerDe Jar to Hive Classpath

In order for the Hive SerDe to work with Solr, the SerDe jar must be added to Hive’s classpath using the hive.aux.jars.path capability. There are several options for this, described below.It’s considered a best practice to use a single directory for all auxiliary jars you may want to add to Hive so you only need to define a single path. However, you must then copy any jars you want to use to that path.

The following options all assume you have created such a directory at /usr/hive/auxlib; if you use another path, update the path in the examples accordingly.

If you use Hive with Ambari (as with the Hortonworks HDP distribution), go to menu:Hive[Configs > Advanced], and scroll down to menu:Advanced hive-env[hive-env template]. Find the section where the HIVE_AUX_JARS_PATH is defined, and add the path to each line which starts with export. What you want will end up looking like:

# Folder containing extra libraries required for hive compilation/execution can be controlled by:
if [ "${HIVE_AUX_JARS_PATH}" != "" ]; then
  if [ -f "${HIVE_AUX_JARS_PATH}" ]; then
    export HIVE_AUX_JARS_PATH=${HIVE_AUX_JARS_PATH},/usr/hive/auxlib
  elif [ -d "/usr/hdp/current/hive-webhcat/share/hcatalog" ]; then
    export HIVE_AUX_JARS_PATH=/usr/hdp/current/hive-webhcat/share/hcatalog/hive-hcatalog-core.jar,/usr/hive/auxlib
  fi
elif [ -d "/usr/hdp/current/hive-webhcat/share/hcatalog" ]; then
  export HIVE_AUX_JARS_PATH=/usr/hdp/current/hive-webhcat/share/hcatalog/hive-hcatalog-core.jar,/usr/hive/auxlib
fi

If not using Ambari or similar cluster management tool, you can add the jar location to hive/conf/hive-site.xml:

<property>
  <name>hive.aux.jars.path</name>
  <value>/usr/hive/auxlib</value>
</property>

Another option is to launch Hive with the path defined with the auxpath variable: hive —auxpath /usr/hive/auxlibThere are also other approaches that could be used. Keep in mind, though, that the jar must be loaded into the classpath, adding it with the ADD JAR function is not sufficient.

Indexing Data with a Hive External Table

Indexing data to Solr or Fusion requires creating a Hive external table. An external table allows the data in the table to be used (read or write) by another system or application outside of Hive.

Indexing Data to Solr

For integration with Solr, the external table allows you to have Solr read from and write to Hive.To create an external table for Solr, you can use a command similar to below. The properties available are described after the example.

hive> CREATE EXTERNAL TABLE solr (id string, field1_s string, field2_i int) <1>
      STORED BY 'com.lucidworks.hadoop.hive.LWStorageHandler' <2>
      LOCATION '/tmp/solr' <3>
      TBLPROPERTIES('solr.server.url' = 'http://localhost:8888/solr', <4>
                    'solr.collection' = 'collection1',
                    'solr.query' = '*:*');

<1> In this example, we have created an external table named “solr”, and defined a set of fields and types for the data we will store in the table. See the section <<Defining Fields for Solr>> below for best practices when naming fields.<2> This defines a custom storage handler (STORED BY 'com.lucidworks.hadoop.hive.LWStorageHandler'), which is one of the classes included with the Hive SerDe jar.<3> The LOCATION indicates the location in HDFS where the table data will be stored. In this example, we have chosen to use /tmp/solr.<4> In the section TBLPROPERTIES, we define several parameters for Solr so the data can be indexed to the right Solr installation and collection. See the section <<Table Properties>> below for details about these parameters.If the table needs to be dropped at a later time, you can use the DROP TABLE command in Hive. This will remove the metadata stored in the table in Hive, but will not modify the underlying data (in this case, the Solr index).

Defining Fields for Solr

When defining the field names for the Hive table, keep in mind that the field types used to define the table in Hive are not sent to Solr when indexing data from a Hive table. The field names are sent, but not the field types. The field types must match or be compatible, however, for queries to complete properly.The reason why this might be a problem is due to a Solr feature called schema guessing. This is Solr’s default mode, and when it is enabled Solr looks at incoming data and makes a best guess at the field type.It can happen that Solr’s guess at the correct type may be different from the type defined in Hive, and if this happens you will get a ClassCastException in response to queries.To avoid this problem, you can use a Solr feature called dynamic fields. These direct Solr to use specific field types based on a prefix or suffix found on an incoming field name, which overrides Solr guessing at the type. Solr includes by default dynamic field rules for nearly all types it supports, so you only need to use the same suffix on your field names in your Hive tables for the correct type to be defined.To illustrate this, note the field names in the table example above:CREATE EXTERNAL TABLE solr (id string, field1_s string, field2_i int)In this example, we have defined the id field as a string, field1_s as a string, and field2_i as an integer. In Solr’s default schema, there is a dynamic field rule that any field with a _s suffix should be a string. Similarly, there is another rule that any field with _i as a suffix should be an integer. This allows us to make sure the field types match.An alternative to this is to disable Solr’s field guessing altogether, but this would require you to create all of your fields in Solr before indexing any content from Hive.For more information about these features and options, please see the following sections of the Apache Solr Reference Guide:

Table Properties

The following parameters can be set when defining the table properties:solr.zkhost: The location of the ZooKeeper quorum if using LucidWorks in SolrCloud mode. If this property is set along with the solr.server.url property, the solr.server.url property will take precedence.solr.server.url: The location of the Solr instance if not using LucidWorks in SolrCloud mode. If this property is set along with the solr.zkhost property, this property will take precedence.solr.collection: The Solr collection for this table. If not defined, an exception will be thrown.solr.query: The specific Solr query to execute to read this table. If not defined, a default of \*:* will be used. This property is not needed when loading data to a table, but is needed when defining the table so Hive can later read the table.lww.commit.on.close: If true, inserts will be automatically committed when the connection is closed. True is the default.lww.jaas.file: Used only when indexing to or reading from a Solr cluster secured with Kerberos. This property defines the path to a JAAS file that contains a service principal and keytab location for a user who is authorized to read from and write to Solr and Hive. The JAAS configuration file must be copied to the same path on every node where a Node Manager is running (i.e., every node where map/reduce tasks are executed). Here is a sample section of a JAAS file:

Client { --<1>
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  keyTab="/data/solr-indexer.keytab" --<2>
  storeKey=true
  useTicketCache=true
  debug=true
  principal="solr-indexer@SOLRSERVER.COM"; --<3>
};

<1> The name of this section of the JAAS file. This name will be used with the lww.jaas.appname parameter. <2> The location of the keytab file. <3> The service principal name. This should be a different principal than the one used for Solr, but must have access to both Solr and Hive.lww.jaas.appname: Used only when indexing to or reading from a Solr cluster secured with Kerberos. This property provides the name of the section in the JAAS file that includes the correct service principal and keytab path.

Indexing Data to Fusion

If you use Lucidworks Fusion, you can index data from Hive to Solr via Fusion’s index pipelines. These pipelines allow you several options for further transforming your data.

If you are using Fusion v3.0.x, you already have the Hive SerDe in Fusion’s ./apps/connectors/resources/lucid.hadoop/jobs directory. The SerDe jar that supports Fusion is v2.2.4 or higher. This was released with Fusion 3.0.If you are using Fusion 3.1.x and higher, you will need to download the Hive SerDe from http://lucidworks.com/connectors/. Choose the proper Hadoop distribution and the resulting .zip file will include the Hive SerDe.A 2.2.4 or higher jar built from this repository will also work with Fusion 2.4.x releases.

This is an example Hive command to create an external table to index documents in Fusion and to query the table later.

hive> CREATE EXTERNAL TABLE fusion (id string, field1_s string, field2_i int)
      STORED BY 'com.lucidworks.hadoop.hive.FusionStorageHandler'
      LOCATION '/tmp/fusion'
      TBLPROPERTIES('fusion.endpoints' = 'http://localhost:8764/api/apollo/index-pipelines/<pipeline>/collections/<collection>/index',
                    'fusion.fail.on.error' = 'false',
                    'fusion.buffer.timeoutms' = '1000',
                    'fusion.batchSize' = '500',
                    'fusion.realm' = 'KERBEROS',
                    'fusion.user' = 'fusion-indexer@FUSIONSERVER.COM',
                    'java.security.auth.login.config' = '/path/to/JAAS/file',
                    'fusion.jaas.appname' = 'FusionClient',
                    'fusion.query.endpoints' = 'http://localhost:8764/api/apollo/query-pipelines/pipeline-id/collections/collection-id',
                    'fusion.query' = '*:*');

In this example, we have created an external table named “fusion”, and defined a custom storage handler (STORED BY 'com.lucidworks.hadoop.hive.FusionStorageHandler') that a class included with the Hive SerDe jar designed for use with Fusion.Note that all of the same caveats about field types discussed in the section <<Defining Fields for Solr>> apply to Fusion as well. In Fusion, however, you have the option of using an index pipeline to perform specific field mapping instead of using dynamic fields.The LOCATION indicates the location in HDFS where the table data will be stored. In this example, we have chosen to use /tmp/fusion.In the section TBLPROPERTIES, we define several properties for Fusion so the data can be indexed to the right Fusion installation and collection:fusion.endpoints: The full URL to the index pipeline in Fusion. The URL should include the pipeline name and the collection data will be indexed to.fusion.fail.on.error: If true, when an error is encountered, such as if a row could not be parsed, indexing will stop. This is false by default.fusion.buffer.timeoutms: The amount of time, in milliseconds, to buffer documents before sending them to Fusion. The default is 1000. Documents will be sent to Fusion when either this value or fusion.batchSize is met.fusion.batchSize: The number of documents to batch before sending the batch to Fusion. The default is 500. Documents will be sent to Fusion when either this value or fusion.buffer.timeoutms is met.fusion.realm: This is used with fusion.user and fusion.password to authenticate to Fusion for indexing data. Two options are supported, KERBEROS or NATIVE. Kerberos authentication is supported with the additional definition of a JAAS file. The properties java.security.auth.login.config and fusion.jaas.appname are used to define the location of the JAAS file and the section of the file to use. Native authentication uses a Fusion-defined username and password. This user must exist in Fusion, and have the proper permissions to index documents.fusion.user: The Fusion username or Kerberos principal to use for authentication to Fusion. If a Fusion username is used ('fusion.realm' = 'NATIVE'), the fusion.password must also be supplied.fusion.password: This property is not shown in the example above. The password for the fusion.user when the fusion.realm is NATIVE.java.security.auth.login.config: This property defines the path to a JAAS file that contains a service principal and keytab location for a user who is authorized to read from and write to Fusion and Hive. The JAAS configuration file must be copied to the same path on every node where a Node Manager is running (i.e., every node where map/reduce tasks are executed). Here is a sample section of a JAAS file:

Client { --<1>
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  keyTab="/data/fusion-indexer.keytab" --<2>
  storeKey=true
  useTicketCache=true
  debug=true
  principal="fusion-indexer@FUSIONSERVER.COM"; --<3>
};

<1> The name of this section of the JAAS file. This name will be used with the fusion.jaas.appname parameter. <2> The location of the keytab file. <3> The service principal name. This should be a different principal than the one used for Fusion, but must have access to both Fusion and Hive. This name is used with the fusion.user parameter described above.fusion.jaas.appname: Used only when indexing to or reading from Fusion when it is secured with Kerberos. This property provides the name of the section in the JAAS file that includes the correct service principal and keytab path.fusion.query.endpoints: The full URL to a query pipeline in Fusion. The URL should include the pipeline name and the collection data will be read from. You should also specify the request handler to be used. If you do not intend to query your Fusion data from Hive, you can skip this parameter.fusion.query: The query to run in Fusion to select records to be read into Hive. This is \*:* by default, which selects all records in the index. If you do not intend to query your Fusion data from Hive, you can skip this parameter.

Query and Insert Data to Hive

Once the table is configured, any syntactically correct Hive query will be able to query the index.For example, to select three fields named “id”, “field1_s”, and “field2_i” from the “solr” table, you would use a query such as:

hive> SELECT id, field1_s, field2_i FROM solr;

Replace the table name as appropriate to use this example with your data.To join data from tables, you can make a request such as:

hive> SELECT id, field1_s, field2_i FROM solr left
      JOIN sometable right
      WHERE left.id = right.id;

And finally, to insert data to a table, simply use the Solr table as the target for the Hive INSERT statement, such as:

hive> INSERT INTO solr
      SELECT id, field1_s, field2_i FROM sometable;

Example Indexing Hive to Solr

Solr includes a small number of sample documents for use when getting started. One of these is a CSV file containing book metadata. This file is found in your Solr installation, at $SOLR_HOME/example/exampledocs/books.csv.Using the sample books.csv file, we can see a detailed example of creating a table, loading data to it, and indexing that data to Solr.

CREATE TABLE books (id STRING, cat STRING, title STRING, price FLOAT, in_stock BOOLEAN, author STRING, series STRING, seq INT, genre STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY ','; --<1>

LOAD DATA LOCAL INPATH '/solr/example/exampledocs/books.csv' OVERWRITE INTO TABLE books; --<2>

CREATE EXTERNAL TABLE solr (id STRING, cat_s STRING, title_s STRING, price_f FLOAT, in_stock_b BOOLEAN, author_s STRING, series_s STRING, seq_i INT, genre_s STRING) --<3>
     STORED BY 'com.lucidworks.hadoop.hive.LWStorageHandler' --<4>
     LOCATION '/tmp/solr' --<5>
     TBLPROPERTIES('solr.zkhost' = 'zknode1:2181,zknode2:2181,zknode3:2181/solr',
                   'solr.collection' = 'gettingstarted',
                   'solr.query' = '*:*', --<6>
                   'lww.jaas.file' = '/data/jaas-client.conf'); --<7>

INSERT OVERWRITE TABLE solr SELECT b.* FROM books b;

<1> Define the table books, and provide the field names and field types that will make up the table. <2> Load the data from the books.csv file. <3> Create an external table named solr, and provide the field names and field types that will make up the table. These will be the same field names as in your local Hive table, so we can index all of the same data to Solr. <4> Define the custom storage handler provided by the {packageUser}-hive-serde-{connectorVersion}.jar. <5> Define storage location in HDFS. <6> The query to run in Solr to read records from Solr for use in Hive. <7> Define the location of Solr (or ZooKeeper if using SolrCloud), the collection in Solr to index the data to, and the query to use when reading the table. This example also refers to a JAAS configuration file that will be used to authenticate to the Kerberized Solr cluster.

Import Data with Pig

You can use Pig to import data into Fusion, using the lucidworks-pig-functions-v2.2.6.jar file found in $FUSION_HOME/apps/connectors/resources/lucid.hadoop/jobs.

Features

Functions to allow Pig scripts to index data to Solr or Fusion.
Supports Kerberos for authentication.
As of v2.2.4, integration with http://lucidworks.com/products is supported, allowing use of Fusion pipelines for further data transformation prior to indexing.

Supported versions are:

Solr 5.x and higher
Pig versions 0.12 through 0.16
Hadoop 3.x
Fusion 2.4.x and higher

Build the Functions Jar

You must use Java 8 or higher to build the .jar.

git submodule init

If a build is happening from a branch, please make sure that solr-hadoop-common is pointing to the correct SHA. (See https://github.com/blog/2104-working-with-submodules for more details.)
```
pig-solr $ git checkout <branch-name>
pig-solr $ cd solr-hadoop-common
pig-solr/solr-hadoop-common $ git checkout <SHA>
pig-solr/solr-hadoop-common $ cd ..
```

The build uses Gradle. However, you do not need Gradle installed before attempting to build.To build the .jar file, run this command:./gradlew clean shadowJar --infoThis will make a .jar file:solr-pig-functions/build/libs/-pig-functions-.jarThe .jar is required to use the Pig functions.

Troubleshooting Clone Issues

If GitHub + SSH is not configured the following exception will be thrown:

    Cloning into 'solr-hadoop-common'...
    Permission denied (publickey).
    fatal: Could not read from remote repository.

    Please make sure you have the correct access rights
    and the repository exists.
    fatal: clone of 'git@github.com:LucidWorks/solr-hadoop-common.git' into submodule path 'solr-hadoop-common' failed

To fix this error, use the https://help.github.com/articles/generating-an-ssh-key/ tutorial.

Available Functions

The Pig functions included in the {packageUser}-pig-functions-{connectorVersion}.jar are three UserDefined Functions (UDF) and two Store functions. These functions are:

com/lucidworks/hadoop/pig/SolrStoreFunc.class
com/lucidworks/hadoop/pig/FusionIndexPipelinesStoreFunc.class
com/lucidworks/hadoop/pig/EpochToCalendar.class
com/lucidworks/hadoop/pig/Extract.class
com/lucidworks/hadoop/pig/Histogram.class

Using the Functions

Register the Functions

There are two approaches to using functions in Pig: REGISTER them in the script, or load them with your Pig command line request.If using REGISTER, the Pig function jars must be put in HDFS in order to be used by your Pig script. It can be located anywhere in HDFS; you can either supply the path in your script or use a variable and define the variable with -p property definition.The example below uses the second approach, loading the jars with the -Dpig.additional.jars system property when launching the script. With this approach, the jars can be located anywhere on the machine where the script will be run.

Indexing Data to Solr

There are a few required parameters for your script to output data to Solr for indexing.These parameters can be defined in the script itself, or turned into variables that are defined each time the script runs. The example Pig script below shows an example of using these parameters with variables.solr.zkhost: The ZooKeeper connection string if using Solr in SolrCloud mode. This should be in the form of server:port,server:port,server:port/chroot.If you are not using SolrCloud, use the solr.server.url parameter instead.solr.server.url: The location of the Solr instance when Solr is running in standalone mode. This should be in the form of \http://server:port/solr.solr.collection: The name of the Solr collection where documents will be indexed.

Indexing to a Kerberos-Secured Solr Cluster

When a Solr cluster is secured with Kerberos for internode communication, Pig scripts must include the full path to a JAAS file that includes the service principal and the path to a keytab file that will be used to index the output of the script to Solr.Two parameters provide the information the script needs to access the JAAS file:lww.jaas.file: The path to the JAAS file that includes a section for the service principal who will write to the Solr indexes. For example, to use this property in a Pig script: set lww.jaas.file ‘/path/to/login.conf’; The JAAS configuration file must be copied to the same path on every node where a Node Manager is running (i.e., every node where map/reduce tasks are executed).lww.jaas.appname: The name of the section in the JAAS file that includes the correct service principal and keytab path. For example, to use this property in a Pig script: set lww.jaas.appname ‘Client’;Here is a sample section of a JAAS file:

Client { --<1>
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  keyTab="/data/solr-indexer.keytab" --<2>
  storeKey=true
  useTicketCache=true
  debug=true
  principal="solr-indexer@SOLRSERVER.COM"; --<3>
};

Indexing to a SSL-Enabled Solr Cluster

When SSL is enabled in a Solr cluster, Pig scripts must include the full paths to the keystore and truststore with their respective passwords.set lww.keystore ‘/path/to/solr-ssl.keystore.jks’ set lww.keystore.password ‘secret’ set lww.truststore ‘/path/to/solr-ssl.truststore.jks’ set lww.truststore.password ‘secret’

The paths (and secret configurations) should be the same in all YARN/MapReduce hosts.

Indexing Data to Fusion

When indexing data to Fusion, there are several parameters to pass with your script in order to output data to Fusion for indexing.These parameters can be made into variables in the script, with the proper values passed on the command line when the script is initiated. The example script below shows how to do this for Solr. The theory is the same for Fusion, only the parameter names would change as appropriate:fusion.endpoints: The full URL to the index pipeline in Fusion. The URL should include the pipeline name and the collection data will be indexed to.fusion.fail.on.error: If true, when an error is encountered, such as if a row could not be parsed, indexing will stop. This is false by default.fusion.buffer.timeoutms: The amount of time, in milliseconds, to buffer documents before sending them to Fusion. The default is 1000. Documents will be sent to Fusion when either this value or fusion.batchSize is met.fusion.batchSize: The number of documents to batch before sending the batch to Fusion. The default is 500. Documents will be sent to Fusion when either this value or fusion.buffer.timeoutms is met.fusion.realm: This is used with fusion.user and fusion.password to authenticate to Fusion for indexing data. Two options are supported, KERBEROS or NATIVE. Kerberos authentication is supported with the additional definition of a JAAS file. The properties java.security.auth.login.config and fusion.jaas.appname are used to define the location of the JAAS file and the section of the file to use. These are described in more detail below. Native authentication uses a Fusion-defined username and password. This user must exist in Fusion, and have the proper permissions to index documents.fusion.user: The Fusion username or Kerberos principal to use for authentication to Fusion. If a Fusion username is used ('fusion.realm' = 'NATIVE'), the fusion.password must also be supplied.fusion.pass: This property is not shown in the example above. The password for the fusion.user when the fusion.realm is NATIVE.

Indexing to a Kerberized Fusion Installation

When Fusion is secured with Kerberos, Pig scripts must include the full path to a JAAS file that includes the service principal and the path to a keytab file that will be used to index the output of the script to Fusion.Additionally, a Kerberos ticket must be obtained on the server for the principal using kinit.java.security.auth.login.config: This property defines the path to a JAAS file that contains a service principal and keytab location for a user who is authorized to write to Fusion. The JAAS configuration file must be copied to the same path on every node where a Node Manager is running (i.e., every node where map/reduce tasks are executed). Here is a sample section of a JAAS file:

Client { --<1>
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  keyTab="/data/fusion-indexer.keytab" --<2>
  storeKey=true
  useTicketCache=true
  debug=true
  principal="fusion-indexer@FUSIONSERVER.COM"; --<3>
};

Sample CSV Script

The following Pig script will take a simple CSV file and index it to Solr.

set solr.zkhost '$zkHost';
set solr.collection '$collection'; -- <1>

A = load '$csv' using PigStorage(',') as (id_s:chararray,city_s:chararray,country_s:chararray,code_s:chararray,code2_s:chararray,latitude_s:chararray,longitude_s:chararray,flag_s:chararray); -- <2>
--dump A;
B = FOREACH A GENERATE $0 as id, 'city_s', $1, 'country_s', $2, 'code_s', $3, 'code2_s', $4, 'latitude_s', $5, 'longitude_s', $6, 'flag_s', $7; -- <3>

ok = store B into 'SOLR' using com.lucidworks.hadoop.pig.SolrStoreFunc(); -- <4>

This relatively simple script is doing several things that help to understand how the Solr Pig functions work.<1> This and the line above define parameters that are needed by SolrStoreFunc to know where Solr is. SolrStoreFunc needs the properties solr.zkhost and solr.collection, and these lines are mapping the zkhost and collection parameters we will pass when invoking Pig to the required properties.
<2> Load the CSV file, the path and name we will pass with the csv parameter. We also define the field names for each column in CSV file, and their types.
<3> For each item in the CSV file, generate a document id from the first field ($0) and then define each field name and value in name, value pairs.
<4> Load the documents into Solr, using the SolrStoreFunc. While we don’t need to define the location of Solr here, the function will use the zkhost and collection properties that we will pass when we invoke our Pig script.

When using SolrStoreFunc, the document ID must be the first field.

When we want to run this script, we invoke Pig and define several parameters we have referenced in the script with the -p option, such as in this command:

./bin/pig -Dpig.additional.jars=/path/to/{packageUser}-pig-functions-{connectorVersion}.jar -p csv=/path/to/my/csv/airports.dat -p zkHost=zknode1:2181,zknode2:2181,zknode3:2181/solr -p collection=myCollection ~/myScripts/index-csv.pig

The parameters to pass are:csv: The path and name of the CSV file we want to process.zkhost: The ZooKeeper connection string for a SolrCloud cluster, in the form of zkhost1:port,zkhost2:port,zkhost3:port/chroot. In the script, we mapped this to the solr.zkhost property, which is required by the SolrStoreFunc to know where to send the output documents.collection: The Solr collection to index into. In the script, we mapped this to the solr.collection property, which is required by the SolrStoreFunc to know the Solr collection the documents should be indexed to.

The zkhost parameter above is only used if you are indexing to a SolrCloud cluster, which uses ZooKeeper to route indexing and query requests.If, however, you are not using SolrCloud, you can use the solrUrl parameter, which takes the location of a standalone Solr instance, in the form of \http://host:port/solr.In the script, you would change the line that maps solr.zkhost to the zkhost property to map solr.server.url to the solrUrl property. For example:set solr.server.url '$solrUrl';

Import Data with the REST API

It is often possible to get documents into Fusion Server by configuring a datasource with the appropriate connector.

But if there are obstacles to using connectors, it can be simpler to index documents with a REST API call to an index profile or pipeline.

Push documents to Fusion using index profiles

Index profiles allow you to send documents to a consistent endpoint (the profile alias) and change the backend index pipeline as needed. The profile is also a simple way to use one pipeline for multiple collections without any one collection “owning” the pipeline.

Send data to an index profile that is part of an app

Accessing an index profile through an app lets a Fusion admin secure and manage all objects on a per-app basis. Security is then determined by whether a user can access an app. This is the recommended way to manage permissions in Fusion.The syntax for sending documents to an index profile that is part of an app is as follows:

curl -u USERNAME:PASSWORD -X POST -H 'content-type: application/json' https://FUSION_HOST:FUSION_PORT/api/apps/APP_NAME/index/INDEX_PROFILE --data-binary @my-json-data.json

Spaces in an app name become underscores. Spaces in an index profile name become hyphens.

To prevent the terminal from displaying all the data and metadata it indexes—useful if you are indexing a large file—you can optionally append ?echo=false to the URL.Be sure to set the content type header properly for the content being sent. Some frequently used content types are:

Text: application/json, application/xml
PDF documents: application/pdf
MS Office:
- DOCX: application/vnd.openxmlformats-officedocument.wordprocessingml.document
- XLSX: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
- PPTX: application/vnd.vnd.openxmlformats-officedocument.presentationml.presentation
- More types: http://filext.com/faq/office_mime_types.php

Example: Send JSON data to an index profile under an app

In $FUSION_HOME/apps/solr-dist/example/exampledocs you can find a few sample documents. This example uses one of these, books.json.To push JSON data to an index profile under an app:

Create an index profile. In the Fusion UI, click Indexing > Index Profiles and follow the prompts.

From the directory containing books.json, enter the following, substituting your values for username, password, and index profile name:

curl -u USERNAME:PASSWORD -X POST -H 'content-type: application/json' https://FUSION_HOST:FUSION_PORT/api/apps/APP_NAME/index/INDEX_PROFILE?echo=false --data-binary @books.json

Test that your data has made it into Fusion:
1. Log into the Fusion UI.
2. Navigate to the app where you sent your data.
3. Navigate to the Query Workbench.
4. Search for \*:*.
5. Select relevant Display Fields, for example author and name.

Example: Send JSON data without defining an app

In most cases it is best to delegate permissions on a per-app basis. But if your use case requires it, you can push data to Fusion without defining an app.To send JSON data without app security, issue the following curl command:

curl -u USERNAME:PASSWORD -X POST -H 'content-type: application/json' https://FUSION_HOST:FUSION_PORT/api/index/INDEX_PROFILE --data-binary @my-json-data.json

Example: Send XML data to an index profile with an app

To send XML data to an app, use the following:

curl -u USERNAME:PASSWORD -X POST -H 'content-type: application/xml' https://FUSION_HOST:FUSION_PORT/api/apps/APP_NAME/index/INDEX_PROFILE --data-binary @my-xml-file.xml

In Fusion 5, documents can be created on the fly using the PipelineDocument JSON notation.

Remove documents

Example 1

The following example removes content:

curl -u USERNAME:PASSWORD -X POST -H 'content-type: application/vnd.lucidworks-document' https://FUSION_HOST:FUSION_PORT/api/apps/APP_NAME/index/INDEX_PROFILE --data-binary @del-json-data.json

Example 2

A more specific example removes data from books.json. To delete “The Lightning Thief” and “The Sea of Monsters” from the index, use their id values in the JSON file.The del-json-data.json file to delete the two books:

[{ "id": "978-0641723445","commands": [{"name": "delete","params": {}}]},{ "id": "978-1423103349","commands": [{"name": "delete","params": {}}, {"name": "commit","params": {}}]}]

The ?echo=false can be used to turn off the response to the terminal.

Example 3

Another example to delete items using the Push API is:

curl -u admin:XXX -X POST  'http://FUSION_HOST:FUSION_PORT/api/apps/APP/index/INDEX' -H 'Content-Type: application/vnd.lucidworks-document' -d '[
  {
    "id": "1663838589-44",
    "commands":
    [
      {
        "name": "delete",
        "params":
        {}
      },
      {
        "name": "commit",
        "params":
        {}
      }
    ]
  }, ...
]'

Send documents to an index pipeline

Although sending documents to an index profile is recommended, if your use case requires it, you can send documents directly to an index pipeline.For more information about index pipeline REST API reference documentation, select the link for your Fusion release:

Specify a parser

When you push data to a pipeline, you can specify the name of the parser by adding a parserId querystring parameter to the URL. For example: https://FUSION_HOST:FUSION_PORT/api/index-pipelines/INDEX_PIPELINE/collections/COLLECTION_NAME/index?parserId=PARSER.If you do not specify a parser, and you are indexing outside of an app (https://FUSION_HOST:FUSION_PORT/api/index-pipelines/...), then the _system parser is used.If you do not specify a parser, and you are indexing in an app context (https://FUSION_HOST:FUSION_PORT/api/apps/APP_NAME/index-pipelines/...), then the parser with the same name as the app is used.

Indexing CSV Files

In the usual case, to index a CSV or TSV file, the file is split into records, one per row, and each row is indexed as a separate document.

LucidAcademyLucidworks offers free training to help you get started.The Course for Ingesting Data focuses on understanding connectors and how to configure datasources, parsers, and index pipelines:

Visit the LucidAcademy to see the full training catalog.

Introduction to Fusion

Getting Data In

Getting Data Out

Operations

Reference

Developer Docs

Neural Hybrid Search

Release Notes