Thursday, July 20, 2017

Securing Apache Hive - part I

This is the first post in a series of articles on securing Apache Hive. In this article we will look at installing Apache Hive and doing some queries on data stored in HDFS. We will not consider any security requirements in this post, but the test deployment will be used by future posts in this series on authenticating and authorizing access to Hive.

1) Install and configure Apache Hadoop

The first step is to install and configure Apache Hadoop. Please follow section 1 of this earlier tutorial for information on how to do this. In addition, we need to configure two extra properties in 'etc/hadoop/core-site.xml':
  • hadoop.proxyuser.$user.groups: *
  • hadoop.proxyuser.$user.hosts: localhost
where "$user" above should be replaced with the user that is going to run the hive server below. As we are not using authentication in this tutorial, this allows the $user to impersonate the "anonymous" user, who will connect to Hive via beeline and run some queries.

Once HDFS has started, we need to create some directories for use by Apache Hive, and change the permissions appropriately:
  • bin/hadoop fs -mkdir -p /user/hive/warehouse /tmp
  • bin/hadoop fs -chmod g+w /user/hive/warehouse /tmp
  • bin/hadoop fs -mkdir /data
The "/data" directory will hold a file which represents the output of a map-reduce job. For the purposes of this tutorial, we will use a sample output of the canonical "Word Count" map-reduce job on some text. The file consists of two columns separated by a tab character, where the left column is the word, and the right column is the total count associated with that word in the original document.

I've uploaded such a sample output here. Download it and upload it to the HDFS data directory:
  • bin/hadoop fs -put output.txt /data
2) Install and configure Apache Hive

Now we will install and configure Apache Hive. Download and extract Apache Hive (2.1.1 was used for the purposes of this tutorial). Set the "HADOOP_HOME" environment variable to point to the Apache Hadoop installation directory above. Now we will configure the metastore and start Hiveserver2:
  • bin/schematool -dbType derby -initSchema
  • bin/hiveserver2
In a separate window, we will start beeline to connect to the hive server, where $user is the user who is running Hadoop (necessary as we are going to create some data in HDFS, and otherwise wouldn't have the correct permissions):
  • bin/beeline -u jdbc:hive2://localhost:10000 -n $user
Once we are connected, then create a Hive table and load the map reduce output data into a new table called "words":
  • create table words (word STRING, count INT) row format delimited fields terminated by '\t' stored as textfile;
  • LOAD DATA INPATH '/data/output.txt' INTO TABLE words;
Now we can run some queries on the data as the anonymous user. Log out of beeline and then back in and run some queries via:
  • bin/beeline -u jdbc:hive2://localhost:10000
  • select * from words where word == 'Dare';

Friday, June 30, 2017

Securing Apache Solr - part III

This is the third post in a series of articles on securing Apache Solr. The first post looked at setting up a sample SolrCloud instance and securing access to it via Basic Authentication. The second post looked at how the Apache Ranger admin service can be configured to store audit information in Apache Solr. In this post we will extend the example in the first article to include authorization, by showing how to create and enforce authorization policies using Apache Ranger.

1) Install the Apache Ranger Solr plugin

The first step is to install the Apache Ranger Solr plugin. Download Apache Ranger and verify that the signature is valid and that the message digests match. Now extract and build the source, and copy the resulting plugin to a location where you will configure and install it:
  • mvn clean package assembly:assembly -DskipTests
  • tar zxvf target/ranger-${version}-solr-plugin.tar.gz
  • mv ranger-${version}-solr-plugin ${ranger.solr.home}
Now go to ${ranger.solr.home} and edit "install.properties". You need to specify the following properties:
  • POLICY_MGR_URL: Set this to "http://localhost:6080"
  • REPOSITORY_NAME: Set this to "solr_service".
  • COMPONENT_INSTALL_DIR_NAME: The location of your Apache Solr server directory
Save "install.properties" and install the plugin as root via "sudo -E ./enable-solr-plugin.sh". Make sure that the user who is running Solr can read the "/etc/ranger/solr_service/policycache". Now follow the first tutorial to get an example SolrCloud instance up and running with a "gettingstarted" collection. We will not enable the authorization plugin just yet.

2) Create authorization policies for Solr using the Apache Ranger Admin service

Now follow the second tutorial to download and install the Apache Ranger admin service. To avoid conflicting with the Solr example we are securing, we will skip the section about auditing to Apache Solr (sections 3 and 4). In addition, in section 5 the "audit_store" property can be left empty, and the Solr audit properties can be omitted. Start the Apache Ranger admin service via: "sudo ranger-admin start", and open a browser at "http://localhost:6080", logging on with "admin/admin" credentials. Click on the "+" button for the Solr service and create a new service with the following properties:
  • Service Name: solr_service
  • Username: alice
  • Password: SolrRocks
  • Solr URL: http://localhost:8983/solr
Hit the "Test Connection" button and it should show that it has successfully connected to Solr. Click "Add" and then click on the "solr_service" link that is subsequently created. We will grant a policy that allows "alice" the ability to read the "gettingstarted" collection. If "alice" is not already created, go to "Settings/User+Groups" and create a new user there. Delete the default policy that is created in the "solr_service" and then click on "Add new policy" and create a new policy called "gettingstarted_policy". For "Solr Collection" enter "g" here and the "gettingstarted" collection should pop up. Add a new "allow condition" granting the user "alice" the "others" and "query" permissions.




3) Test authorization using the Apache Ranger plugin for Solr

Now we are ready to enable the Apache Ranger authorization plugin for Solr. Download the following security configuration which enables Basic Authentication in Solr as well as the Apache Ranger authorization plugin:
Now upload this configuration to the Apache Zookeeper instance that is running with Solr:
  • server/scripts/cloud-scripts/zkcli.sh -zkhost localhost:9983 -cmd putfile /security.json security.json
 Now let's try to query the "gettingstarted" collection as 'alice':
  • curl -u alice:SolrRocks http://localhost:8983/solr/gettingstarted/query?q=author_s:Arthur+Miller
This should be successful. However, authorization will fail for the case of "bob":
  • curl -u bob:SolrRocks http://localhost:8983/solr/gettingstarted/query?q=author_s:Arthur+Miller
In addition, although "alice" can query the collection, she can't write to it, and the following query will return 403:
  • curl -u alice:SolrRocks http://localhost:8983/solr/gettingstarted/update -d '[ {"id" : "book4", "title_t" : "Hamlet", "author_s" : "William Shakespeare"}]'

Tuesday, June 27, 2017

Securing Apache Solr - part II

This is the second post in a series of articles on securing Apache Solr. The first post looked at setting up a sample SolrCloud instance and securing access to it via Basic Authentication. In this post we will temporarily deviate from the concept of "securing Apache Solr", and instead look at how the Apache Ranger admin service can be configured to store audit information in Apache Solr.

1) Download and extract the Apache Ranger admin service

The first step is to download the source code, as well as the signature file and associated message digests (all available on the download page). Verify that the signature is valid and that the message digests match. Now extract and build the source, and copy the resulting admin archive to a location where you wish to install the UI:
  • tar zxvf apache-ranger-incubating-1.0.0.tar.gz
  • cd apache-ranger-incubating-1.0.0
  • mvn clean package assembly:assembly 
  • tar zxvf target/ranger-1.0.0-admin.tar.gz
  • mv ranger-1.0.0-admin ${rangerhome}
2) Install MySQL

The Apache Ranger Admin UI requires a database to keep track of users/groups as well as policies for various big data projects that you are securing via Ranger. For the purposes of this tutorial, we will use MySQL. Install MySQL in $SQL_HOME and start MySQL via:
  • sudo $SQL_HOME/bin/mysqld_safe --user=mysql
Now you need to log on as the root user and create two users for Ranger. We need a root user with admin privileges (let's call this user "admin") and a user for the Ranger Schema (we'll call this user "ranger"):
  • CREATE USER 'admin'@'localhost' IDENTIFIED BY 'password';
  • GRANT ALL PRIVILEGES ON * . * TO 'admin'@'localhost' WITH GRANT OPTION;
  • CREATE USER 'ranger'@'localhost' IDENTIFIED BY 'password';
  • FLUSH PRIVILEGES;
Finally,  download the JDBC driver jar for MySQL and put it in ${rangerhome}.

3) Configure Apache Solr to support auditing from Ranger

Before installing the Apache Ranger admin service we will need to configure Apache Solr. The Apache Ranger admin service ships with a script to make this easier to configure. Edit 'contrib/solr_for_audit_setup/install.properties' with the following properties:
  • SOLR_USER/SOLR_GROUP - the user/group you are running solr as
  • SOLR_INSTALL_FOLDER - Where you have extracted Solr to as per the first tutorial.
  • SOLR_RANGER_HOME - Where to install the Ranger configuration for Solr auditing.
  • SOLR_RANGER_PORT - The port to be used (8983 as per the first tutorial).
  • SOLR_DEPLOYMENT - solrcloud
  • SOLR_HOST_URL - http://localhost:8983
  • SOLR_ZK - localhost:2181
Make sure that the user running Solr has permission to write to the value configured for "SOLR_LOG_FOLDER" (/var/log/solr/ranger_audits). Now in 'contrib/solr_for_audit_setup' run 'sudo -E ./setup.sh'. The Solr configuration is now copied to $SOLR_RANGER_HOME.

4) Start Apache Zookeeper and SolrCloud

Before starting Apache Solr we will need to start Apache Zookeeper. Download Apache Zookeeper and start it on port 2181 via (this step was not required in the previous tutorial as we were launching SolrCloud with an embedded Zookeeper instance):
  • bin/zkServer.sh start
As per the first post, we want to secure access to SolrCloud via Basic Authentication (note that this is only recently fixed in Apache Ranger). So follow the steps in this post to upload the security.json to Zookeeper via:
  • server/scripts/cloud-scrip/zkcli.sh -zkhost localhost:2181 -cmd putfile /security.json security.json
Start Solr as follows in the '${SOLR_RANGER_HOME}/ranger_audit_server/scripts' directory:
  • ./add_ranger_audits_conf_to_zk.sh 
  • ./start_solr.sh
Edit 'create_ranger_audits_collection.sh' and change 'curl --negotiate -u :' to 'curl -u "alice:SolrRocks"'. Save it and then run:
  • ./create_ranger_audits_collection.sh
5) Install the Apache Ranger Admin UI

Edit ${rangerhome}/install.properties and make the following changes:
  • Change SQL_CONNECTOR_JAR to point to the MySQL JDBC driver jar that you downloaded above.
  • Set (db_root_user/db_root_password) to (admin/password)
  • Set (db_user/db_password) to (ranger/password)
  • audit_solr_urls: http://localhost:8983/solr/ranger_audits
  • audit_solr_user: alice
  • audit_solr_password: SolrRocks
  • audit_solr_zookeepers: localhost:2181
Now you can run the setup script via "sudo -E ./setup.sh". When this is done then start the Apache Ranger admin service via: "sudo ranger-admin start".

6) Test that auditing is working correctly in the Ranger Admin service

Open a browser and navigate to "http://localhost:6080". Try to log on first using some made up credentials. Then log in using "admin/admin". Click on the "Audit" tab and then select "Login Sessions". You should see the incorrect and the correct login attempts, meaning that ranger is successfully storing and retrieving audit information in Solr:


Monday, June 26, 2017

Securing Apache Solr - part I

This is the first post in a series of articles on securing Apache Solr. In this post we will look at deploying an example SolrCloud instance and securing access to it via basic authentication.

1) Install and deploy a SolrCloud example

Download and extract Apache Solr (6.6.0 was used for the purpose of this tutorial). Now start SolrCloud via:
  • bin/solr -e cloud
Accept all of the default options. This creates a cluster of two nodes, with a collection "gettingstarted" split into two shards and two replicas per-shard. A web interface is available after startup at: http://localhost:8983/solr/.

Once the cluster is up and running we can post some data to the collection we have created via the REST interface:
  • curl http://localhost:8983/solr/gettingstarted/update -d '[ {"id" : "book1", "title_t" : "The Merchant of Venice", "author_s" : "William Shakespeare"}]'
  • curl http://localhost:8983/solr/gettingstarted/update -d '[ {"id" : "book2", "title_t" : "Macbeth", "author_s" : "William Shakespeare"}]'
  • curl http://localhost:8983/solr/gettingstarted/update -d '[ {"id" : "book3", "title_t" : "Death of a Salesman", "author_s" : "Arthur Miller"}]'
We can search the REST interface to for example return all entries by William Shakespeare as follows:
  • curl http://localhost:8983/solr/gettingstarted/query?q=author_s:William+Shakespeare
2) Authenticating users to our SolrCloud instance

Now that our SolrCloud instance is up and running, let's look at how we can secure access to it, by using HTTP Basic Authentication to authenticate our REST requests. Download the following security configuration which enables Basic Authentication in Solr:
Two users are defined - "alice" and "bob" - both with password "SolrRocks". Now upload this configuration to the Apache Zookeeper instance that is running with Solr:
  • server/scripts/cloud-scripts/zkcli.sh -zkhost localhost:9983 -cmd putfile /security.json security.json
Now try to run the search query above again using Curl. A 401 error will be returned. Once we specify the correct credentials then the request will work as expected, e.g.:
  • curl -u alice:SolrRocks http://localhost:8983/solr/gettingstarted/query?q=author_s:Arthur+Miller

Thursday, June 22, 2017

SSO support for Apache Syncope REST services

Apache Syncope has recently added SSO support for its REST services in the 2.0.3 release. Previously, access to the REST services of Syncope was via HTTP Basic Authentication. From the 2.0.3 release, SSO support is available using JSON Web Tokens (JWT). In this post, we will look at how this works and how it can be configured.

1) Obtaining an SSO token from Apache Syncope

As stated above, in the past it was necessary to supply HTTP Basic Authentication credentials when invoking on the REST API. Let's look at an example using curl. Assume we have a running Apache Syncope instance with a user "alice" with password "ecila". We can make a GET request to the user self service via:
  • curl -u alice:ecila http://localhost:8080/syncope/rest/users/self
It may be inconvenient to supply user credentials on each request or the authentication process might not scale very well if we are authenticating the password to a backend resource. From Apache Syncope 2.0.3, we can instead get an SSO token by sending a POST request to "accessTokens/login" as follows:
  • curl -I -u alice:ecila -X POST http://localhost:8080/syncope/rest/accessTokens/login
The response contains two headers:
  • X-Syncope-Token: A JWT token signed according to the JSON Web Signature (JWS) spec.
  • X-Syncope-Token-Expire: The expiry date of the token
The token in question is signed using the (symmetric) "HS512" algorithm. It contains the subject "alice" and the issuer of the token ("ApacheSyncope"), as well as a random token identifier, and timestamps that indicate when the token was issued, when it expires, and when it should not be accepted before.

The signing key and the issuer name can be changed by editing 'security.properties' and specifying new values for 'jwsKey' and 'jwtIssuer'. Please note that it is critical to change the signing key from the default value! It is also possible to change the signature algorithm from the next 2.0.4 release via a custom 'securityContext.xml' (see here). The default lifetime of the token (120 minutes) can be changed via the "jwt.lifetime.minutes" configuration property for the domain.

2) Using the SSO token to invoke on a REST service

Now that we have an SSO token, we can use it to invoke on a REST service instead of specifying our username and password as before. For Syncope 2.0.3 only, the header name is the same as the header name above "X-Syncope-Token". From Syncope 2.0.4 onwards, the header name is "Authorization: Bearer <token>", e.g.:
  • curl -H "Authorization: Bearer eyJ0e..." http://localhost:8080/syncope/rest/users/self
The signature is first checked on the token, then the issuer is verified so that it matches what is configured, and then the expiry and not-before dates are checked. If the identifier matches that of a saved access token then authentication is successful.

Finally, SSO tokens can be seen in the admin console under "Dashboard/Access Token", where they can be manually revoked by the admin user:


Monday, June 19, 2017

Querying Apache HBase using Talend Open Studio for Big Data

Recent blog posts have described how to set up authorization for Apache HBase using Apache Ranger. However the posts just covered inputing and reading data using the HBase Shell. In this post, we will show how Talend Open Studio for Big Data can be used to read data stored in Apache HBase. This post is along the same lines of other recent tutorials on reading data from Kafka and HDFS.

1) HBase setup

Follow this tutorial on setting up Apache HBase in standalone mode, and creating a 'data' table with some sample values using the HBase Shell.

2) Download Talend Open Studio for Big Data and create a job

Now we will download Talend Open Studio for Big Data (6.4.0 was used for the purposes of this tutorial). Unzip the file when it is downloaded and then start the Studio using one of the platform-specific scripts. It will prompt you to download some additional dependencies and to accept the licenses. Click on "Create a new job" called "HBaseRead". In the search bar on the right-hand side, enter "hbase" and hit enter. Drag "tHBaseConnection" and "tHBaseInput" onto the palette, as well as "tLogRow".

"tHBaseConnection" is used to set up the connection to "HBase", "tHBaseInput" uses the connection to read data from HBase, and "tLogRow" will log the data that was read so that we can see that the job ran successfully. Right-click on "tHBaseConnection" and select "Trigger/On Subjob Ok" and drag the resulting arrow to the "tHBaseInput" component. Now right click on "tHBaseInput" and select "Row/Main" and drag the arrow to "tLogRow".
3) Configure the components

Now let's configure the individual components. Double click on "tHBaseConnection" and select the distribution "Hortonworks" and Version "HDP V2.5.0" (from an earlier tutorial we are using HBase 1.2.6). We are not using Kerberos here so we can skip the rest of the security configuration. Now double click on "tHBaseInput". Select the "Use an existing connection" checkbox. Now hit "Edit Schema" and add two entries to map the column we created in two different column families: "c1" which matches DB "col1" of type String, and "c2" which matches DB "col1" of type String.


Select "data" for the table name back in tHBaseInput and add a mapping for "c1" to "colfam1", and "c2" to "colfam2".


Now we are ready to run the job. Click on the "Run" tab and then hit the "Run" button. You should see "val1" and "val2" appear in the console window.

Wednesday, June 14, 2017

Securing Apache HBase - part II

This is the second (and final for now) post in a short series of blog posts on securing Apache HBase. The first post looked at how to set up a standalone instance of HBase and how to authorize access to a table using Apache Ranger. In this post, we will look at how Apache Ranger can create "tag" based authorization policies for Apache HBase using Apache Atlas.

1) Start Apache Atlas and create entities/tags for HBase

First let's look at setting up Apache Atlas. Download the latest released version (0.8-incubating) and extract it. Build the distribution that contains an embedded HBase and Solr instance via:
  • mvn clean package -Pdist,embedded-hbase-solr -DskipTests
The distribution will then be available in 'distro/target/apache-atlas-0.8-incubating-bin'. To launch Atlas, we need to set some variables to tell it to use the local HBase and Solr instances:
  • export MANAGE_LOCAL_HBASE=true
  • export MANAGE_LOCAL_SOLR=true
Now let's start Apache Atlas with 'bin/atlas_start.py'. Open a browser and go to 'http://localhost:21000/', logging on with credentials 'admin/admin'. Click on "TAGS" and create a new tag called "customer_data". Now click on "Search" and then follow the "Create new entity" link of type "hbase_table" with the following parameters:
  • Name: data
  • QualifiedName: data@cl1
  • Uri: data
Now add the 'customer_data' tag to the entity that we have created.

2) Use the Apache Ranger TagSync service to import tags from Atlas into Ranger

To create tag based policies in Apache Ranger, we have to import the entity + tag we have created in Apache Atlas into Ranger via the Ranger TagSync service. After building Apache Ranger then extract the file called "target/ranger-<version>-tagsync.tar.gz". Edit 'install.properties' as follows:
  • Set TAG_SOURCE_ATLAS_ENABLED to "false"
  • Set TAG_SOURCE_ATLASREST_ENABLED to  "true" 
  • Set TAG_SOURCE_ATLASREST_DOWNLOAD_INTERVAL_IN_MILLIS to "60000" (just for testing purposes)
  • Specify "admin" for both TAG_SOURCE_ATLASREST_USERNAME and TAG_SOURCE_ATLASREST_PASSWORD
Save 'install.properties' and install the tagsync service via "sudo ./setup.sh". Start the Apache Ranger admin service via "sudo ranger-admin start" and then the tagsync service via "sudo ranger-tagsync-services.sh start".

3) Create Tag-based authorization policies in Apache Ranger

Now let's create a tag-based authorization policy in the Apache Ranger admin UI. Click on "Access Manager" and then "Tag based policies". Create a new Tag service called "HBaseTagService". Create a new policy for this service called "CustomerDataPolicy". In the "TAG" field enter a "c" and the "customer_data" tag should pop up, meaning that it was successfully synced in from Apache Atlas. Create an "Allow" condition for the user "bob" with the "Read" permission for the "HBase" component.

We also need to do is to go back to the Resource based policies and edit "cl1_hbase" and select the tag service we have created above. Now we are ready to test the authorization policy we have created with HBase. Start the shell as "bob" and we should be able to read the table we created in the first tutorial:
  • sudo -E -u bob bin/hbase shell
  • scan 'data'

Tuesday, June 13, 2017

Securing Apache HBase - part I

This is the first in a short series of blog posts on securing Apache HBase. HBase is a column-based database that facilitates random read/write access to data stored in the Hadoop FileSystem (HDFS). In this post we will focus on setting up a standalone instance of Apache HBase, and then demonstrate how to use Apache Ranger to authorize access to a HBase table.

1) Install Apache HBase

Download Apache HBase (version 1.2.6 was used for the purposes of this tutorial) and extract it. As stated above, we will set up a standalone version of HBase, which means that HBase itself and Apache Zookeeper run in a single JVM, and data is stored in the local filesystem instead of HDFS. Normally we would authenticate users via Kerberos, but as we are just running HBase in standalone mode, we will focus solely on authorization in this series of tutorials. Start HBase via:
  • bin/start-hbase.sh
Then start the shell and create a sample table called "data", with two column families, and add some rows to the table:
  • bin/hbase shell
  • create 'data', 'colfam1', 'colfam2'
  • put 'data', 'row1', 'colfam1:col1', 'val1'
  • put 'data', 'row1', 'colfam2:col1', 'val2'
  • scan 'data'
The latter command will print out the values stored in the table. Next we will look at using Apache Ranger to restrict access to the 'data' table to authorized users only.

2) Install the Apache Ranger HBase plugin 

Download Apache Ranger and verify that the signature is valid and that the message digests match. Extract and build the source, and copy the resulting plugin to a location where you will configure and install it, e.g.:
  • mvn clean package assembly:assembly -DskipTests
  • tar zxvf target/ranger-1.0.0-SNAPSHOT-hbase-plugin.tar.gz
  • mv ranger-1.0.0-SNAPSHOT-hbase-plugin ${ranger.hbase.home}
Now go to ${ranger.hbase.home} and edit "install.properties". You need to specify the following properties:
  • POLICY_MGR_URL: Set this to "http://localhost:6080"
  • REPOSITORY_NAME: Set this to "cl1_hbase".
  • COMPONENT_INSTALL_DIR_NAME: The location of your Apache HBase installation
Save "install.properties" and install the plugin as root via "sudo ./enable-hbase-plugin.sh". The Apache Ranger HBase plugin should now be successfully installed. The ranger plugin will try to store policies by default in "/etc/ranger/cl1_hbase/policycache". As we installed the plugin as "root" make sure that this directory is accessible to the user that is running HBase.

3) Configure authorization policies in the Apache Ranger Admin UI 

The next step is to create some authorization policies for Apache HBase in the Apache Ranger admin service. Please refer to this blog post for information on how to install the Apache Ranger admin service. Assuming the admin service is already installed, start it via "sudo ranger-admin start". Open a browser and log on to "localhost:6080" with the credentials "admin/admin".

Create a new HBase service, adding the following configuration items to the default values:
  • Service Name: cl1_hbase
  • Username/Password: admin
  • hbase.zookeeper.quorum: localhost
Click on "Test Connection" (if HBase is running) to verify that the connection is successful (note: only works from 1.0.0 onwards - see RANGER-1640) and then save the service. Click on "cl1_hbase" and edit the default policy which has been created, and add the user running HBase to the "Allow Condition" permission.

Now we will add a new authorization policy to test access to HBase. Under "Settings + Users/Groups" add two new users called "alice" and "bob", and also create these new users in your local system. Now we can create a new authorization policy to grant "alice" the "Read" permission for the "data" table (all column families and columns).



4) Testing authorization in HBase

The policy we have created above will get downloaded and enforced by the Ranger HBase plugin we installed into HBase. Restart HBase before proceeding further (if it was running with the Ranger plugin before downloading the policy which granted the user running HBase "admin" privileges, then HBase might not be working properly). Now start the shell as "alice" and try to read the table we created earlier:
  • sudo -E -u alice bin/hbase shell
  • scan 'data'
This should work due to the authorization policy we created. However "alice" should not be allowed to write to "data", e.g the following should result in a "AccessDeniedException":
  • put 'data', 'row1', 'colfam1:col1', 'val3'

Tuesday, June 6, 2017

Securing Apache Storm - part IV

This is the fourth and final post in a series of blog posts on securing Apache Storm. The first post looked at setting up a simple Storm cluster that authenticates users via Kerberos, and deploying a topology. The second post looked at deploying the Storm UI using Kerberos, and accessing it via a REST client. The third post looked at how to use Apache Ranger to authorize access to Apache Storm.  In this post, we will look at how Apache Ranger can create "tag" based authorization policies for Apache Storm using Apache Atlas.

1) Start Apache Atlas and create entities/tags for Storm

First let's look at setting up Apache Atlas. Download the latest released version (0.8-incubating) and extract it. Build the distribution that contains an embedded HBase and Solr instance via:
  • mvn clean package -Pdist,embedded-hbase-solr -DskipTests
The distribution will then be available in 'distro/target/apache-atlas-0.8-incubating-bin'. To launch Atlas, we need to set some variables to tell it to use the local HBase and Solr instances:
  • export MANAGE_LOCAL_HBASE=true
  • export MANAGE_LOCAL_SOLR=true
Now let's start Apache Atlas with 'bin/atlas_start.py'. Open a browser and go to 'http://localhost:21000/', logging on with credentials 'admin/admin'. Click on "TAGS" and create a new tag called "user_topologies".  Unlike for HDFS or Kafka, Atlas doesn't provide an easy way to create a Storm Entity in the UI. Instead we can use the following json file to create a Storm Entity for "*" topologies:

You can upload it to Atlas via:
  • curl -v -H 'Accept: application/json, text/plain, */*' -H 'Content-Type: application/json;  charset=UTF-8' -u admin:admin -d @storm-create.json http://localhost:21000/api/atlas/entities
Once the new entity has been uploaded, then you can search for it in the Atlas UI, then click on "+" beside "Tags" and associate the new entity with the "user_topologies" tag.

2) Use the Apache Ranger TagSync service to import tags from Atlas into Ranger

To create tag based policies in Apache Ranger, we have to import the entity + tag we have created in Apache Atlas into Ranger via the Ranger TagSync service. After building Apache Ranger then extract the file called "target/ranger-<version>-tagsync.tar.gz". Edit 'install.properties' as follows:
  • Set TAG_SOURCE_ATLAS_ENABLED to "false"
  • Set TAG_SOURCE_ATLASREST_ENABLED to  "true" 
  • Set TAG_SOURCE_ATLASREST_DOWNLOAD_INTERVAL_IN_MILLIS to "60000" (just for testing purposes)
  • Specify "admin" for both TAG_SOURCE_ATLASREST_USERNAME and TAG_SOURCE_ATLASREST_PASSWORD
Save 'install.properties' and install the tagsync service via "sudo ./setup.sh". Start the Apache Ranger admin service via "sudo ranger-admin start" and then the tagsync service via "sudo ranger-tagsync-services.sh start".

3) Create Tag-based authorization policies in Apache Ranger

Now let's create a tag-based authorization policy in the Apache Ranger admin UI. Click on "Access Manager" and then "Tag based policies". Create a new Tag service called "StormTagService". Create a new policy for this service called "UserTopologiesPolicy". In the "TAG" field enter a "u" and the "user_topologies" tag should pop up, meaning that it was successfully synced in from Apache Atlas. Create an "Allow" condition for the user "alice" with all of the component permissions for "Storm":


We also need to do is to go back to the Resource based policies and edit "cl1_storm" and select the tag service we have created above. Finally, edit the existing "cl1_storm" policy created as par of the previous tutorials, and remove the permissions for "alice" there, so that we can be sure that authorization is working correctly. Then follow the first tutorial and verify that "alice" is authorized to deploy a topology as per the tag-based authorization policy we have created in Ranger.

Friday, June 2, 2017

Securing Apache Storm - part III

This is the third in a series of blog posts on securing Apache Storm. The first post looked at setting up a simple Storm cluster that authenticates users via Kerberos, and deploying a topology. The second post looked at deploying the Storm UI using Kerberos, and accessing it via a REST client. Thus far we have only looked at how to authenticate users to Storm using Kerberos. In this post we will look at how to use Apache Ranger to authorize access to Apache Storm.

1) Install the Apache Ranger Storm plugin
 
Follow the steps in the first tutorial (parts 1 - 3) to setup the Apache Kerby testcase, Apache Zookeeper instance, and the Apache Storm distribution, if you have not done this already. Now we will install the Apache Ranger Storm plugin. If you want to be able to download the topologies from Storm to Ranger when creating policies, then follow the second tutorial to start the Storm UI.

Download Apache Ranger and verify that the signature is valid and that the message digests match. Due to some bugs that were fixed for the installation process, I am using version 1.0.0-SNAPSHOT in this post. Now extract and build the source, and copy the resulting plugin to a location where you will configure and install it:
  • mvn clean package assembly:assembly -DskipTests
  • tar zxvf target/ranger-1.0.0-SNAPSHOT-storm-plugin.tar.gz
  • mv ranger-1.0.0-SNAPSHOT-storm-plugin ${ranger.storm.home}
Now go to ${ranger.storm.home} and edit "install.properties". You need to specify the following properties:
  • POLICY_MGR_URL: Set this to "http://localhost:6080"
  • REPOSITORY_NAME: Set this to "cl1_storm".
  • COMPONENT_INSTALL_DIR_NAME: The location of your Apache Storm installation
Save "install.properties" and install the plugin as root via "sudo ./enable-storm-plugin.sh". The Apache Ranger Storm plugin should now be successfully installed. Now start Kerby, Zookeeper and Storm as covered in the first tutorial.

2) Create authorization policies in the Apache Ranger Admin console

Next we will use the Apache Ranger admin console to create authorization policies for Apache Storm. Follow the steps in this tutorial to install the Apache Ranger admin service. To retrieve the running topologies from Apache Storm, then you need to configure Kerberos appropriately for Apache Ranger. You can first point to the Kerby krb5.conf via:
  • export JAVA_OPTS="-Djava.security.krb5.conf=/path.to./kerby.project/target/krb5.conf"
Start the Apache Ranger admin service with "sudo -E ranger-admin start" and open a browser and go to "http://localhost:6080/" and log on with "admin/admin". Add a new Storm service with the following configuration values:
  • Service Name: cl1_storm
  • Username: storm-client
  • Password: storm-client
  • Nimbus URL: http://localhost:8080
Click on "Test Connection" to verify that we can connect successfully to Storm  + then save the new service. Now click on the "cl1_storm" service that we have created. Edit the existing policy for the "*" Storm topology, adding the user "alice" (create this user if you have not done so already under "Settings, Users/Groups") to all of the available permissions.

3) Testing authorization in Storm

Now let's test the Ranger authorization policy we created above in action. The Ranger authorization plugin will pull policies from the Admin service every 30 seconds by default. For the "cl1_storm" example above, they are stored in "/etc/ranger/cl1_storm/policycache/" by default. Make sure that the user you are running Storm as can access this directory. To test authorization follow step 4 in the first tutorial, but use the user "storm-client" instead (and "storm_client.keytab"). You should see an authorization exception. Now try again with user "alice" (and "alice.keytab") and authorization should succeed.

Wednesday, May 31, 2017

Securing Apache Storm - part II

This is the second in a series of tutorials on securing Apache Storm. The first post looked at setting up a simple Storm cluster that authenticates users via Kerberos, and deploying a topology. Apache Storm also ships with a UI (and REST API) that can be used to download configuration, start/stop topologies, etc. This post looks at deploying the Storm UI using Kerberos, and accessing it via a REST client.

1) Configure the Apache Storm UI

The first step is to follow the previous tutorial to deploy the Apache Kerby KDC, to configure Apache Zookeeper, and to download and deploy Apache Storm (sections 1-3). Note that there is a bug in Kerby that is not yet fixed in the 1.0.0 release that you might run in to when using curl (see below), depending on whether the MIT libraries are installed or not. In additional to the principals listed in the last post, the Kerby deployment test for Storm also contains a principal for the Storm UI (HTTP/localhost@storm.apache.org).

Now edit 'conf/storm.yaml' and add the following properties:
  • ui.filter: "org.apache.hadoop.security.authentication.server.AuthenticationFilter"
  •  ui.filter.params:
    • "type": "kerberos"
    • "kerberos.principal": "HTTP/localhost@storm.apache.org"
    • "kerberos.keytab": "/path.to.kerby.project/target/http.keytab"
    • "kerberos.name.rules": "RULE:[2:$1@$0]([jt]t@.*EXAMPLE.COM)s/.*/$MAPRED_USER/ RULE:[2:$1@$0]([nd]n@.*EXAMPLE.COM)s/.*/$HDFS_USER/DEFAULT"
Start the UI with:
  • bin/storm ui
2) Invoke on the Storm UI REST API

We will invoke on the Storm UI REST API using "curl" on the command line. This can be done as follows:
  • export KRB5_CONFIG=/path.to.kerby.project/target/krb5.conf
  • kinit -k -t /path.to.kerby.project/target/alice.keytab alice
  • curl --negotiate -u : -b ~/cookiejar.txt -c ~/cookiejar.txt http://localhost:8080/api/v1/cluster/configuration
You should see the cluster configuration in JSON format if the call is successful.

Friday, May 26, 2017

Securing Apache Storm - part I

This is the first tutorial in a planned three part series on securing Apache Storm. In this post we will look at setting up a simple Storm cluster that authenticates users via Kerberos, and how to run a simple topology on it. Future posts will cover authorization using Apache Ranger. For more information on how to setup Kerberos for Apache Storm, please see the following documentation.

1) Set up a KDC using Apache Kerby

As for other kerberos-related tutorials that I have written on this blog, we will use a github project I wrote that uses Apache Kerby to start up a KDC:
  • bigdata-kerberos-deployment: This project contains some tests which can be used to test kerberos with various big data deployments, such as Apache Hadoop etc.
The KDC is a simple junit test that is available here. To run it just comment out the "org.junit.Ignore" annotation on the test method. It uses Apache Kerby to define the following principals:
  • zookeeper/localhost@storm.apache.org
  • zookeeper-client@storm.apache.org
  • storm/localhost@storm.apache.org
  • storm-client@@storm.apache.org
  • alice@storm.apache.org
Keytabs are created in the "target" folder. Kerby is configured to use a random port to lauch the KDC each time, and it will create a "krb5.conf" file containing the random port number in the target directory.

2) Download and configure Apache Zookeeper

Apache Storm uses Apache Zookeeper to help coordinate the cluster. Download Apache Zookeeper (this tutorial used 3.4.10) and extract it to a local directory. Configure Zookeeper to use Kerberos by adding a new file 'conf/zoo.cfg' with the following properties:
  • dataDir=/tmp/zookeeper
  • clientPort=2181
  • authProvider.1 = org.apache.zookeeper.server.auth.SASLAuthenticationProvider
  • requireClientAuthScheme=sasl 
  • jaasLoginRenew=3600000 
Now create 'conf/zookeeper.jaas' with the following content:

Server {
        com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="/path.to.kerby.project/target/zookeeper.keytab" storeKey=true principal="zookeeper/localhost";
};

Before launching Zookeeper, we need to point to the JAAS configuration file above and also to the krb5.conf file generated in the Kerby test-case above. Add a new file 'conf/java.env' adding the SERVER_JVMFLAGS property to the classpath with:
  • -Djava.security.auth.login.config=/path.to.zookeeper/conf/zookeeper.jaas
  • -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf".
Start Zookeeper via:
  • bin/zkServer.sh start
3) Download and configure Apache Storm

Now download and extract the Apache Storm distribution (1.1.0 was used in this tutorial). Edit 'conf/storm.yaml' and edit the following properties:
  • For "storm.zookeeper.servers" add "- localhost"
  • nimbus.seeds: ["localhost"]
  • storm.thrift.transport: "org.apache.storm.security.auth.kerberos.KerberosSaslTransportPlugin"
  • java.security.auth.login.config: "/path.to.storm/conf/storm.jaas"
  • storm.zookeeper.superACL: "sasl:storm"
  • nimbus.childopts: "-Djava.security.auth.login.config=/path.to.storm/conf/storm.jaas -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf" 
  • ui.childopts: "-Djava.security.auth.login.config=/path.to.storm/conf/storm.jaas -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf" 
  • supervisor.childopts: "-Djava.security.auth.login.config=/path.to.storm/conf/storm.jaas -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf"
Create a file called 'conf/storm.jaas' with the content:

Client {
    com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="/path.to.kerby.project/target/zookeeper_client.keytab" storeKey=true principal="zookeeper-client";
};

StormClient {  
    com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="path.to.kerby.project/target/storm_client.keytab" storeKey=true principal="storm-client" serviceName="storm";
};

StormServer {
    com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="path.to.kerby.project/target/storm.keytab" storeKey=true principal="storm/localhost@storm.apache.org";
};

'Client' is used to communicate with Zookeeper, 'StormClient' is used by the supervisor nodes and 'StormServer' is used by nimbus. Now start Nimbus and a supervisor node via:
  • bin/storm nimbus
  • bin/storm supervisor
4) Deploy a Topology

As we have the Storm cluster up and running, the next task is to deploy a Topology to it. For this we will need to use another Storm distribution, so extract Storm again to another directory. Edit 'conf/storm.yaml' and edit the following properties:
  • For "storm.zookeeper.servers" add "- localhost"
  • nimbus.seeds: ["localhost"]
  • storm.thrift.transport: "org.apache.storm.security.auth.kerberos.KerberosSaslTransportPlugin"
  • java.security.auth.login.config: "/path.to.storm.client/conf/storm.jaas"
Create a file called 'conf/storm.jaas' with the content:

StormClient {
            com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useTicketCache=true serviceName="storm";
};

Note that we are not using keytabs here, but instead a ticket cache. Now edit 'conf/storm_env.ini' and add:
  • STORM_JAR_JVM_OPTS:-Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf
Now that we have everything set up, it's time to deploy a topology to our cluster. I have a simple Storm topology that wires a WordSpout + WordCounterBolt into a topology that can be used for this in github here. Check this project out from github + build it via "mvn assembly:assembly". We will need a Kerberos ticket store in our ticket cache to deploy the job:
  • export KRB5_CONFIG=/path.to.kerby.project/target/krb5.conf
  • kinit -k -t /path.to.kerby.project/target/alice.keytab alice
Finally we can submit our topology:
  • bin/storm jar /path.to.storm.project/target/bigdata-storm-demo-1.0-jar-with-dependencies.jar  org.apache.coheigea.bigdata.storm.StormMain /path.to.storm.project/target/test-classes/words.txt
If you take a look at the logs in the nimbus distribution you should see that the topology has run correctly, e.g. 'logs/workers-artifacts/mytopology-1-1495813912/6700/worker.log'.

Tuesday, May 23, 2017

Configuring Kerberos for Kafka in Talend Open Studio for Big Data

A recent blog post showed how to use Talend Open Studio for Big Data to access data stored in HDFS, where HDFS had been configured to authenticate users using Kerberos. In this post we will follow a similar setup, to see how to create a job in Talend Open Studio for Big Data to read data from an Apache Kafka topic using kerberos.

1) Kafka setup

Follow a recent tutorial to setup an Apache Kerby based KDC testcase and to configure Apache Kafka to require kerberos for authentication. Create a "test" topic and write some data to it, and verify with the command-line consumer that the data can be read correctly.

2) Download Talend Open Studio for Big Data and create a job

Now we will download Talend Open Studio for Big Data (6.4.0 was used for the purposes of this tutorial). Unzip the file when it is downloaded and then start the Studio using one of the platform-specific scripts. It will prompt you to download some additional dependencies and to accept the licenses. Click on "Create a new job" called "KafkaKerberosRead". 
In the search bar under "Palette" on the right hand side enter "kafka" and hit enter. Drag "tKafkaConnection" and "tKafkaInput" to the middle of the screen. Do the same for "tLogRow":
We now have all the components we need to read data from the Kafka topic. "tKafkaConnection" will be used to configure the connection to Kafka. "tKafkaInput" will be used to read the data from the "test" topic, and finally "tLogRow" will just log the data so that we can be sure that it was read correctly. The next step is to join the components up. Right click on "tKafkaConnection" and select "Trigger/On Subjob Ok" and drag the resulting line to "tKafkaInput". Right click on "tKafkaInput" and select "Row/Main" and drag the resulting line to "tLogRow":

3) Configure the components

Now let's configure the individual components. Double click on "tKafkaConnection". If a message appears that informs you that you need to install additional jars, then click on "Install". Select the version of Kafka that corresponds to the version you are using (if it doesn't match then select the most recent version). For the "Zookeeper quorum list" property enter "localhost:2181". For the "broker list" property enter "localhost:9092".

Now we will configure the kerberos related properties of "tKafkaConnection". Select the "Use kerberos authentication" checkbox and some additional configuration properties will appear. For "JAAS configuration path" you need to enter the path of the "client.jaas" file as described in the tutorial to set up the Kafka test-case. You can leave "Kafka brokers principal name" property as the default value ("kafka"). Finally, select the "Set kerberos configuration path" property and enter the path of the "krb5.conf" file supplied in the target directory of the Apache Kerby test-case.



Now click on "tKafkaInput". Select the checkbox for "Use an existing connection" + select the "tKafkaConnection" component in the resulting component list. For "topic name" specify "test". The "Consumer group id" can stay as the default "mygroup".

Now we are ready to run the job. Click on the "Run" tab and then hit the "Run" button. Send some data via the producer to the "test" topic and you should see the data appear in the Run Window in the Studio.

Monday, May 22, 2017

Security advisories issued for Apache CXF Fediz

Two security advisories were recently issued for Apache CXF Fediz. In addition to fixing these issues, the recent releases of Fediz impose tighter security constraints in some areas by default compared to older releases. In this post I will document the advisories and the other security-related changes in the recent Fediz releases.

1) Security Advisories

The first security advisory is CVE-2017-7661: "The Apache CXF Fediz Jetty and Spring plugins are vulnerable to CSRF attacks.". Essentially, both the Jetty 8/9 and Spring Security 2/3 plugins are subject to a CSRF-style vulnerability when the user doesn't complete the authentication process. In addition, the Jetty plugins are vulnerable even if the user does first complete the authentication process, but only the root context is available as part of this attack.

The second advisory is CVE-2017-7662: "The Apache CXF Fediz OIDC Client Registration Service is vulnerable to CSRF attacks". The OIDC client registration service is a simple web application that allows the creation of clients for OpenId Connect, as well as a number of other administrative tasks. It is vulnerable to CSRF attacks, where a malicious application could take advantage of an existing session to make changes to the OpenId Connect clients that are stored in the IdP.

2) Fediz IdP security constraints

This section only concerns the WS-Federation (and SAML-SSO) IdP in Fediz. The WS-Federation RP application sends its address via the 'wreply' parameter to the IdP. For SAML SSO, the address to reply to is taken from the consumer service URL of the SAML SSO Request. Previously, the Apache CXF Fediz IdP contained an optional 'passiveRequestorEndpointConstraint' configuration value in the 'ApplicationEntity', which allows the admin to specify a regular expression constraint on the 'wreply' URL.

From Fediz 1.4.0, 1.3.2 and 1.2.4, a new configuration option is available in the 'ApplicationEntity' called 'passiveRequestorEndpoint'. If specified, this is directly matched against the 'wreply' parameter. In a change that breaks backwards compatibility, but that is necessary for security reasons, one of 'passiveRequestorEndpointConstraint' or 'passiveRequestorEndpoint must be specified in the 'ApplicationEntity' configuration. This ensures that the user cannot be redirected to a malicious client. Similarly, new configuration options are available called 'logoutEndpoint' and 'logoutEndpointConstraint' which validate the 'wreply' parameter in the case of redirecting the user after logging out, one of which must be specified.

3) Fediz RP security constraints

This section only concerns the WS-Federation RP plugins available in Fediz. When the user tries to log out of the Fediz RP application, a 'wreply' parameter can be specified to give the address that the Fediz IdP can redirect to after logout is complete. The old functionality was that if 'wreply' was not specified, then the RP plugin instead used the value from the 'logoutRedirectTo' configuration parameter.

From Fediz 1.4.0, 1.3.2 and 1.2.4, a new configuration option is available called 'logoutRedirectToConstraint'. If a 'wreply' parameter is presented, then it must match the regular expression that is specified for 'logoutRedirectToConstraint', otherwise the 'wreply' value is ignored and it falls back to 'logoutRedirectTo'. 

Thursday, May 18, 2017

Configuring Kerberos for HDFS in Talend Open Studio for Big Data

A recent series of blog posts showed how to install and configure Apache Hadoop as a single node cluster, and how to authenticate users via Kerberos and authorize them via Apache Ranger. Interacting with HDFS via the command line tools as shown in the article is convenient but limited. Talend offers a freely-available product called Talend Open Studio for Big Data which you can use to interact with HDFS instead (and many other components as well). In this article we will show how to access data stored in HDFS that is secured with Kerberos as per the previous tutorials.

1) HDFS setup

To begin with please follow the first tutorial to install Hadoop and to store the LICENSE.txt in a '/data' folder. Then follow the fifth tutorial to set up an Apache Kerby based KDC testcase and configure HDFS to authenticate users via Kerberos. To test everything is working correctly on the command line do:
  • export KRB5_CONFIG=/pathtokerby/target/krb5.conf
  • kinit -k -t /pathtokerby/target/alice.keytab alice
  • bin/hadoop fs -cat /data/LICENSE.txt
2) Download Talend Open Studio for Big Data and create a job

Now we will download Talend Open Studio for Big Data (6.4.0 was used for the purposes of this tutorial). Unzip the file when it is downloaded and then start the Studio using one of the platform-specific scripts. It will prompt you to download some additional dependencies and to accept the licenses. Click on "Create a new job" called "HDFSKerberosRead". In the search bar under "Palette" on the right hand side enter "tHDFS" and hit enter. Drag "tHDFSConnection" and "tHDFSInput" to the middle of the screen. Do the same for "tLogRow":
We now have all the components we need to read data from HDFS. "tHDFSConnection" will be used to configure the connection to Hadoop. "tHDFSInput" will be used to read the data from "/data" and finally "tLogRow" will just log the data so that we can be sure that it was read correctly. The next step is to join the components up. Right click on "tHDFSConnection" and select "Trigger/On Subjob Ok" and drag the resulting line to "tHDFSInput". Right click on "tHDFSInput" and select "Row/Main" and drag the resulting line to "tLogRow":
3) Configure the components

Now let's configure the individual components. Double click on "tHDFSConnection". For the "version", select the "Hortonworks" Distribution with version HDP V2.5.0 (we are using the original Apache distribution as part of this tutorial, but it suffices to select Hortonworks here). Under "Authentication" tick the checkbox called "Use kerberos authentication". For the Namenode principal specify "hdfs/localhost@hadoop.apache.org". Select the checkbox marked "Use a keytab to authenticate". Select "alice" as the principal and "<path.to.kerby.project>/target/alice.keytab" as the "Keytab":
Now click on "tHDFSInput". Select the checkbox for "Use an existing connection" + select the "tHDFSConnection" component in the resulting component list. For "File Name" specify the file we want to read: "/data/LICENSE.txt":
Now click on "Edit schema" and hit the "+" button. This will create a "newColumn" column of type "String". We can leave this as it is, because we are not doing anything with the data other than logging it. Save the job. Now the only thing that remains is to point to the krb5.conf file that is generated by the Kerby project. Click on "Window/Preferences" at the top of the screen. Select "Talend" and "Run/Debug". Add a new JVM argument: "-Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf":

Now we are ready to run the job. Click on the "Run" tab and then hit the "Run" button. If everything is working correctly, you should see the contents of "/data/LICENSE.txt" displayed in the Run window.

Monday, May 15, 2017

Securing Apache Kafka with Kerberos

Last year, I wrote a series of blog articles based on securing Apache Kafka. The articles covered how to secure access to the Apache Kafka broker using TLS client authentication, and how to implement authorization policies using Apache Ranger and Apache Sentry. Recently I wrote another article giving a practical demonstration how to secure HDFS using Kerberos. In this post I will look at how to secure Apache Kafka using Kerberos, using a test-case based on Apache Kerby. For more information on securing Kafka with kerberos, see the Kafka security documentation.

1) Set up a KDC using Apache Kerby

A github project that uses Apache Kerby to start up a KDC is available here:
  • bigdata-kerberos-deployment: This project contains some tests which can be used to test kerberos with various big data deployments, such as Apache Hadoop etc.
The KDC is a simple junit test that is available here. To run it just comment out the "org.junit.Ignore" annotation on the test method. It uses Apache Kerby to define the following principals:
  • zookeeper/localhost@kafka.apache.org
  • kafka/localhost@kafka.apache.org
  • client@kafka.apache.org
Keytabs are created in the "target" folder. Kerby is configured to use a random port to lauch the KDC each time, and it will create a "krb5.conf" file containing the random port number in the target directory. 

2) Configure Apache Zookeeper

Download Apache Kafka and extract it (0.10.2.1 was used for the purposes of this tutorial). Edit 'config/zookeeper.properties' and add the following properties:
  • authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
  • requireClientAuthScheme=sasl 
  • jaasLoginRenew=3600000
Now create 'config/zookeeper.jaas' with the following content:

Server {
        com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="/path.to.kerby.project/target/zookeeper.keytab" storeKey=true principal="zookeeper/localhost";
};

Before launching Zookeeper, we need to point to the JAAS configuration file above and also to the krb5.conf file generated in the Kerby test-case above. This can be done by setting the "KAFKA_OPTS" system property with the JVM arguments:
  • -Djava.security.auth.login.config=/path.to.zookeeper/config/zookeeper.jaas 
  • -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf
Now start Zookeeper via:
  • bin/zookeeper-server-start.sh config/zookeeper.properties 
3) Configure Apache Kafka broker

Create 'config/kafka.jaas' with the content:

KafkaServer {
            com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="/path.to.kerby.project/target/kafka.keytab" storeKey=true principal="kafka/localhost";
};

Client {
        com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="/path.to.kerby.project/target/kafka.keytab" storeKey=true principal="kafka/localhost";
};

The "Client" section is used to talk to Zookeeper. Now edit  'config/server.properties' and add the following properties:
  • listeners=SASL_PLAINTEXT://localhost:9092
  • security.inter.broker.protocol=SASL_PLAINTEXT 
  • sasl.mechanism.inter.broker.protocol=GSSAPI 
  • sasl.enabled.mechanisms=GSSAPI 
  • sasl.kerberos.service.name=kafka 
We will just concentrate on using SASL for authentication, and hence we are using "SASL_PLAINTEXT" as the protocol. For "SASL_SSL" please follow the keystore generation as outlined in the following article. Again, we need to set the "KAFKA_OPTS" system property with the JVM arguments:
  • -Djava.security.auth.login.config=/path.to.kafka/config/kafka.jaas 
  • -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf
Now we can start the server and create a topic as follows:
  • bin/kafka-server-start.sh config/server.properties
  • bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
4) Configure Apache Kafka producers/consumers

To make the test-case simpler we added a single principal "client" in the KDC for both the producer and consumer. Create a file called "config/client.jaas" with the content:

KafkaClient {
        com.sun.security.auth.module.Krb5LoginModule required refreshKrb5Config=true useKeyTab=true keyTab="/path.to.kerby.project/target/client.keytab" storeKey=true principal="client";
};

Edit *both* 'config/producer.properties' and 'config/consumer.properties' and add:
  • security.protocol=SASL_PLAINTEXT
  • sasl.mechanism=GSSAPI 
  • sasl.kerberos.service.name=kafka
Now set the "KAFKA_OPTS" system property with the JVM arguments:
  • -Djava.security.auth.login.config=/path.to.kafka/config/client.jaas 
  • -Djava.security.krb5.conf=/path.to.kerby.project/target/krb5.conf
We should now be all set. Start the producer and consumer via:
  • bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test --producer.config config/producer.properties
  • bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning --consumer.config config/consumer.properties --new-consumer

Tuesday, May 9, 2017

Securing Apache Hadoop Distributed File System (HDFS) - part VI

This is the sixth and final article in a series of posts on securing HDFS. In the second and third posts we looked at how to use Apache Ranger to authorize access to data stored in HDFS. In the fifth post, we looked at how to configure HDFS to authenticate users via Kerberos. In this post we will combine both scenarios, that is we will use Apache Ranger to authorize access to HDFS, which is secured using Kerberos.

1) Authenticating to Apache Ranger

Follow the fifth tutorial to set up HDFS using Kerberos for authentication. Then follow the second tutorial to install the Apache Ranger HDFS plugin. The Ranger HDFS plugin will not be able to download new policies from Apache Ranger, as we have not configured Ranger to be able to authenticate clients via Kerberos. Edit 'conf/ranger-admin-site.xml' in the Apache Ranger Admin service and edit the following properties:
  • ranger.spnego.kerberos.principal: HTTP/localhost@hadoop.apache.org
  • ranger.spnego.kerberos.keytab: Path to Kerby ranger.keytab
  • hadoop.security.authentication: kerberos
Now we need to configure Kerberos to use the krb5.conf file generated by Apache Kerby:
  • export JAVA_OPTS="-Djava.security.krb5.conf=<path to Kerby target/krb5.conf"
Start the Apache Ranger admin service ('sudo -E ranger-admin start' to pass the JAVA_OPTS variable through) and edit the "cl1_hadoop" service that was created in the second tutorial. Under "Add New Configurations" add the following:
  • policy.download.auth.users: hdfs
The Ranger HDFS policy should be able to download the policies now from the Ranger Admin service and apply authorization accordingly.

2) Authenticating to HDFS

As we have configured HDFS to require Kerberos, we won't be able to see the HDFS directories in the Ranger Admin service when creating policies any more, without making some changes to enable the Ranger Admin service to authenticate to HDFS. Edit 'conf/ranger-admin-site.xml' in the Apache Ranger Admin service and edit the following properties:
  • ranger.lookup.kerberos.principal: ranger/localhost@hadoop.apache.org
  • ranger.lookup.kerberos.keytab: Path to Kerby ranger.keytab
Edit the 'cl1_hadoop' policy that we created in the second tutorial and click on 'Test Connection'. This should fail as Ranger is not configured to authenticate to HDFS. Add the following properties:
  • Authentication Type: Kerberos
  • dfs.datanode.kerberos.principal: hdfs/localhost
  • dfs.namenode.kerberos.principal: hdfs/localhost
  • dfs.secondary.namenode.kerberos.principal: hdfs/localhost
Now 'Test Connection' should be successful.

Friday, May 5, 2017

Using SASL to secure the the data transfer protocol in Apache Hadoop

The previous blog article showed how to set up a pseudo-distributed Apache Hadoop cluster such that clients are authenticated using Kerberos. The DataNode that we configured authenticates itself by using privileged ports configured in the properties "dfs.datanode.address" and "dfs.datanode.http.address". This requires building and configuring JSVC as well as making sure that we can ssh to localhost without a password as root. An alternative solution (as noted in the article) is to use SASL to secure the data transfer protocol. Here we will briefly show how to do this, building on the configuration given in the previous post.

1) Configuring Hadoop to use SASL for the data transfer protocol

Follow section (2) of the previous post to configure Hadoop to authenticate users via Kerberos. We need to make the following changes to 'etc/hadoop/hdfs-site.xml':
  • dfs.datanode.address: Change the port number here to be a non-privileged port.
  • dfs.datanode.http.address: Change the port number here to be a non-privileged port.
We also need add the following properties to 'etc/hadoop/hdfs-site.xml':
  • dfs.data.transfer.protection: integrity.
  • dfs.http.policy: HTTPS_ONLY.
Edit 'etc/hadoop/hadoop-env.sh' and comment out the values we added for:
  • HADOOP_SECURE_DN_USER
  • JSVC_HOME
2) Configure SSL keys in ssl-server.xml

The next step is to configure some SSL keys in 'etc/hadoop/ssl-server.xml'. We'll use some sample keys that are used in Apache CXF to run the systests for the purposes of this dem. Download cxfca.jks and bob.jks into 'etc/hadoop'. Now edit 'etc/hadoop/ssl-server.xml' and define the following properties:
  • ssl.server.truststore.location: etc/hadoop/cxfca.jks
  • ssl.server.truststore.password: password
  • ssl.server.keystore.location: etc/hadoop/bob.jks
  • ssl.server.keystore.password: password
  • ssl.server.keystore.keypassword: password
3) Launch Kerby and HDFS and test authorization

Now that we have hopefully configured everything correctly it's time to launch the Kerby based KDC and HDFS. Start Kerby by running the JUnit test as described in the first section of the previous article. Now start HDFS via:
  • sbin/start-dfs.sh
Note that 'sudo sbin/start-secure-dns.sh' is not required as we are now using SASL for the data transfer protocol. Now we can read the file we added to "/data" in the previous article as "alice":
  • export KRB5_CONFIG=/pathtokerby/target/krb5.conf
  • kinit -k -t /pathtokerby/target/alice.keytab alice
  • bin/hadoop fs -cat /data/LICENSE.txt

Thursday, May 4, 2017

Securing Apache Hadoop Distributed File System (HDFS) - part V

This is the fifth in a series of blog posts on securing HDFS. The first post described how to install Apache Hadoop, and how to use POSIX permissions and ACLs to restrict access to data stored in HDFS. The second post looked at how to use Apache Ranger to authorize access to data stored in HDFS. The third post looked at how Apache Ranger can create "tag" based authorization policies for HDFS using Apache Atlas. The fourth post looked at how to implement transparent encryption for HDFS using Apache Ranger. Up to now, we have not shown how to authenticate users, concentrating only on authorizing local access to HDFS. In this post we will show how to configure HDFS to authenticate users via Kerberos.

1) Set up a KDC using Apache Kerby

If we are going to configure Apache Hadoop to use Kerberos to authenticate users, then we need a Kerberos Key Distribution Center (KDC). Typically most documentation revolves around installing the MIT Kerberos server, adding principals, and creating keytabs etc. However, in this post we will show a simpler way of getting started by using a pre-configured maven project that uses Apache Kerby. Apache Kerby is a subproject of the Apache Directory project, and is a complete open-source KDC written entirely in Java.

A github project that uses Apache Kerby to start up a KDC is available here:
  • bigdata-kerberos-deployment: This project contains some tests which can be used to test kerberos with various big data deployments, such as Apache Hadoop etc.
The KDC is a simple junit test that is available here. To run it just comment out the "org.junit.Ignore" annotation on the test method. It uses Apache Kerby to define the following principals:
  • alice@hadoop.apache.org
  • bob@hadoop.apache.org
  • hdfs/localhost@hadoop.apache.org
  • HTTP/localhost@hadoop.apache.org
Keytabs are created in the "target" folder for "alice", "bob" and "hdfs" (where the latter has both the hdfs/localhost + HTTP/localhost principals included). Kerby is configured to use a random port to lauch the KDC each time, and it will create a "krb5.conf" file containing the random port number in the target directory. So all we need to do is to point Hadoop to the keytabs that were generated and the krb5.conf, and it should be able to communicate correctly with the Kerby-based KDC.

2) Configure Hadoop to authenticate users via Kerberos

Download and configure Apache Hadoop as per the first tutorial. For now, we will not enable the Ranger authorization plugin, but rather secure access to the "/data" directory using ACLs, as described in section (3) of the first tutorial, such that "alice" has permission to read the file stored in "/data" but "bob" does not. The next step is to configure Hadoop to authenticate users via Kerberos.

Edit 'etc/hadoop/core-site.xml' and adding the following property name/values:
  • hadoop.security.authentication: kerberos
Next edit 'etc/hadoop/hdfs-site.xml' and add the following property name/values to configure Kerberos for the namenode:
  • dfs.namenode.keytab.file: Path to Kerby hdfs.keytab (see above).
  • dfs.namenode.kerberos.principal: hdfs/localhost@hadoop.apache.org
  • dfs.namenode.kerberos.internal.spnego.principal: HTTP/localhost@hadoop.apache.org
Add the exact same property name/values for the secondary namenode, except using the property name "secondary.namenode" instead of "namenode". We also need to configure Kerberos for the datanode:
  • dfs.datanode.data.dir.perm: 700
  • dfs.datanode.address: 0.0.0.0:1004
  • dfs.datanode.http.address: 0.0.0.0:1006
  • dfs.web.authentication.kerberos.principal: HTTP/localhost@hadoop.apache.org
  • dfs.datanode.keytab.file: Path to Kerby hdfs.keytab (see above).
  • dfs.datanode.kerberos.principal: hdfs/localhost@hadoop.apache.org
  • dfs.block.access.token.enable: true 
As we are not using SASL to secure the the data transfer protocol (see here), we need to download and configure JSVC into JSVC_HOME. Then edit 'etc/hadoop/hadoop-env.sh' and add the following properties:
  • export HADOOP_SECURE_DN_USER=(the user you are running HDFS as)
  • export JSVC_HOME=(path to JSVC as above)
  • export HADOOP_OPTS="-Djava.security.krb5.conf=<path to Kerby target/krb5.conf"
You also need to make sure that you can ssh to localhost as "root" without specifying a password.

3) Launch Kerby and HDFS and test authorization

Now that we have hopefully configured everything correctly it's time to launch the Kerby based KDC and HDFS. Start Kerby by running the JUnit test as described in the first section. Now start HDFS via:
  • sbin/start-dfs.sh
  • sudo sbin/start-secure-dns.sh
Now let's try to read the file in "/data" using "bin/hadoop fs -cat /data/LICENSE.txt". You should see an exception as we have no credentials. Let's try to read as "alice" now:
  • export KRB5_CONFIG=/pathtokerby/target/krb5.conf
  • kinit -k -t /pathtokerby/target/alice.keytab alice
  • bin/hadoop fs -cat /data/LICENSE.txt
This should be successful. However the following should result in a "Permission denied" message:
  • kdestroy
  • kinit -k -t /pathtokerby/target/bob.keytab bob
  • bin/hadoop fs -cat /data/LICENSE.txt

Wednesday, April 26, 2017

Securing Apache Hadoop Distributed File System (HDFS) - part IV

This is the fourth in a series of blog posts on securing HDFS. The first post described how to install Apache Hadoop, and how to use POSIX permissions and ACLs to restrict access to data stored in HDFS. The second post looked at how to use Apache Ranger to authorize access to data stored in HDFS. The third post looked at how Apache Ranger can create "tag" based authorization policies for HDFS using Apache Atlas. In this post I will look at how you can implement transparent encryption in HDFS using the Apache Ranger Key Management Service (KMS).

1) Install and Configure the Apache Ranger KMS

If you have not done so already, then follow the instructions in this tutorial to install the Apache Ranger admin service, and then start it via "sudo ranger-admin start". Open a browser and go to "http://localhost:6080/". Log on with "admin/admin" and click on "Settings". Create a new user corresponding to the name of the user which starts HDFS.

The next step is to install the Apache Ranger KMS. Please follow step (2) in a blog post I wrote last year about this. When installation is complete, then start the KMS service with "sudo ranger-kms start". Log out of the Admin UI and then log back in again with the credentials "keyadmin/keyadmin". Click on the "+" button on the "KMS" tab to create a new KMS Service. Specify the following values:
  • Service Name: kmsdev
  • KMS URL: kms://http@localhost:9292/kms
  • Username: keyadmin
  • Password: keyadmin
When the "kmsdev" service has been created then click on it and edit the default policy that has been created. Edit the existing "allow condition" for "hdfs" adding in the user that will be starting HDFS (if not the "hdfs" user itself). Also grant the "CREATE" permission to that user so that we can create keys from the command line, and the "DECRYPT EEK" permission, so that the user can decrypt the data encryption key:


2) Create an encryption zone in HDFS

In your Hadoop distribution (after first following the steps in the first post), edit 'etc/hadoop/core-site.xml' and add the following property:
  • hadoop.security.key.provider.path - kms://http@localhost:9292/kms
Similarly, edit 'etc/hadoop/hdfs-site.xml' and add the following property:
  • dfs.encryption.key.provider.uri - kms://http@localhost:9292/kms
Start HDFS via 'sbin/start-dfs.sh'. Let's create a new encryption key called "enckey" as follows:
  • bin/hadoop key create enckey
If you go back to the Ranger Admin UI and click on "Encryption / Key Manager" and select the "kmsdev" service, you should be able to see the new key that was created. Now let's create a new encryption zone in HDFS as follows:
  • bin/hadoop fs -mkdir /zone
  • bin/hdfs crypto -createZone -keyName enckey -path /zone
  • bin/hdfs crypto -listZones
That's it! We can put data into the '/zone' directory and it will be encrypted by a key which in turn is encrypted by the key we have created and stored in the Ranger KMS.

Friday, April 21, 2017

Securing Apache Hadoop Distributed File System (HDFS) - part III

This is the third in a series of posts on securing HDFS. The first post described how to install Apache Hadoop, and how to use POSIX permissions and ACLs to restrict access to data stored in HDFS. The second post looked at how to use Apache Ranger to authorize access to data stored in HDFS. In this post we will look at how Apache Ranger can create "tag" based authorization policies for HDFS using Apache Atlas. For information on how to create tag-based authorization policies for Apache Kafka, see a post I wrote earlier this year.

The Apache Ranger admin console allows you to create security policies for HDFS by associating a user/group with some permissions (read/write/execute) and a resource, such as a directory or file. This is called a "Resource based policy" in Apache Ranger. An alternative is to use a "Tag based policy", which instead associates the user/group + permissions with a "tag". You can create and manage tags in Apache Atlas, and Apache Ranger supports the ability to imports tags from Apache Atlas via a tagsync service, something we will cover in this post.

1) Start Apache Atlas and create entities/tags for HDFS

First let's look at setting up Apache Atlas. Download the latest released version (0.8-incubating) and extract it. Build the distribution that contains an embedded HBase and Solr instance via:
  • mvn clean package -Pdist,embedded-hbase-solr -DskipTests
The distribution will then be available in 'distro/target/apache-atlas-0.8-incubating-bin'. To launch Atlas, we need to set some variables to tell it to use the local HBase and Solr instances:
  • export MANAGE_LOCAL_HBASE=true
  • export MANAGE_LOCAL_SOLR=true
Now let's start Apache Atlas with 'bin/atlas_start.py'. Open a browser and go to 'http://localhost:21000/', logging on with credentials 'admin/admin'. Click on "TAGS" and create a new tag called "Data".  Click on "Search" and the "Create new entity" link. Select an entity type of "hdfs_path" with the following values:
  • QualifiedName: data@cl1
  • Name: Data
  • Path: /data
Once the new entity has been created, then click on "+" beside "Tags" and associate the new entity with the "Data" tag.

2) Use the Apache Ranger TagSync service to import tags from Atlas into Ranger

To create tag based policies in Apache Ranger, we have to import the entity + tag we have created in Apache Atlas into Ranger via the Ranger TagSync service. First, start the Apache Ranger admin service and rename the HDFS service we created in the previous tutorial from "HDFSTest" to "cl1_hadoop". This is because the Tagsync service will sync tags into the Ranger service that corresponds to the suffix of the qualified name of the tag with "_hadoop". Also edit 'etc/hadoop/ranger-hdfs-security.xml' in your Hadoop distribution and change the "ranger.plugin.hdfs.service.name" to "cl1_hadoop". Also change the "ranger.plugin.hdfs.policy.cache.dir" along the same lines. Finally, make sure the directory '/etc/ranger/cl1_hadoop/policycache' exists and the user you are running Hadoop as can write and read from this directory.

After building Apache Ranger then extract the file called "target/ranger-<version>-tagsync.tar.gz". Edit 'install.properties' as follows:
  • Set TAG_SOURCE_ATLAS_ENABLED to "false"
  • Set TAG_SOURCE_ATLASREST_ENABLED to  "true"
  • Set TAG_SOURCE_ATLASREST_DOWNLOAD_INTERVAL_IN_MILLIS to "60000" (just for testing purposes)
  • Specify "admin" for both TAG_SOURCE_ATLASREST_USERNAME and TAG_SOURCE_ATLASREST_PASSWORD
Save 'install.properties' and install the tagsync service via "sudo ./setup.sh". It can now be started via "sudo ranger-tagsync-services.sh start".

3) Create Tag-based authorization policies in Apache Ranger

Now let's create a tag-based authorization policy in the Apache Ranger admin UI. Click on "Access Manager" and then "Tag based policies". Create a new Tag service called "HDFSTagService". Create a new policy for this service called "DataPolicy". In the "TAG" field enter a capital "D" and the "Data" tag should pop up, meaning that it was successfully synced in from Apache Atlas. Create an "Allow" condition for the user "bob" with component permission of "HDFS" and "read" and "execute":


The last thing we need to do is to go back to the Resource based policies and edit "cl1_hadoop" and select the tag service we have created above.

4) Testing authorization in HDFS using our tag based policy

Wait until the Ranger authorization plugin syncs the new authorization policies from the Ranger Admin service and then we can test authorization. In the previous tutorial we showed that the file owner and user "alice" can read the data stored in '/data', but "bob" could not. Now we should be able to successfully read the data as "bob" due to the tag based authorization policy we have created:
  • sudo -u bob bin/hadoop fs -cat /data/LICENSE.txt