animesh kumar

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Posts Tagged ‘Cassandra

Fiddling with Cassandra 0.7-beta2

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I have been dilly-dallying with Cassandra 0.7 for quite some time. My intensions were to build Cassandra 0.7 support into Kundera (a JPA 1.0 compliant ORM library to work with Cassandra). I must admit that often times I was very upset about the lack of documentation on Cassandra and libraries that I had planned to use, Pelops and Hector. So I decided that I should post my findings for your help.

Now since Cassandra 0.7 beta-2 has been released, I will concentrate my talk around this release.

Installing Cassandra 0.7

  • Download 0.7.0-beta2 (released on 2010-10-01) from here: http://cassandra.apache.org/download/
  • Extract the jar to some location say, D:\apache-cassandra-0.7.0-beta2
  • Set CASSANDRA_HOME environment variable to D:\apache-cassandra-0.7.0-beta2
  • You can also update you PATH variable to include $CASSANDRA_HOME/bin
  • Now, to start the server you would need to run this command:
    > cassandra -start

That’s it.

Okay, since you’ve gotten the basics right. I would like to tell you few important things about this new Cassandra release.

  1. Unlike .6.x versions, 0.7.x employs YAML instead of XML, that is, you are going to find cassandra.yaml instead of storage-conf.xml.
  2. 0.7 allows you to manage entire cluster, Keyspaces, Column Families everything from Thrift API.
  3. There is also support for Apache Avro. (I haven’t explored this though, so no more comment)
  4. 0.7 comes with secondary index features. What does it mean? It means, you can look for your data not just by Row Identifier, but also by Column Values. Interesting huh?

If you look into cassandra.yaml, you will find a default Keyspace1 and few Column Families too, but Cassandra doesn’t load them. I am not sure why. Theoretically, everything defined in the yaml file should have been created at the start. I am going to dig around this. Anyways for now, let’s create some Keyspaces and few Column Families ourselves. We can use Thrift API (and Cassandra client which uses Thrift itself) or JMX interface.

Dealing with Cassandra Client

Cassandra comes with a command line interface tool cassandra-cli. This tool is really really impressive. You should certainly spend some time with it.

  • Start the client,
    > cassandra-cli
  • Connect to server,
    > [default@unknown] connect localhost/9160
  • Create a new keyspace, (I picked this up from cassandra.yaml)
    > [default@unknown] create keyspace Keyspace1 with replication_factor=1
  • Create Column Families,
    > [default@unknown] use Keyspace1
    > [default@Keyspace1] create column family Standard1 with column_type = ‘Standard’ and comparator = ‘BytesType’
  • Describe keyspace,
    > [default@Keyspace1] describe keyspace Keyspace1

And so on. Use ‘help’ to learn more about cassandra-cli.

JConsole

As I mentioned above, you can also use JMX to check what Keyspaces and Column Families exist in your server. But there is a little problem. Cassandra does not come with the mx4j-tools.jar, so you need to download and copy this jar to Cassandra’s lib folder. Download it from here:  http://www.java2s.com/Code/Jar/MNOPQR/Downloadmx4jtoolsjar.htm

Now, just run ‘jconsole’ and pick ‘org.apache.cassandra.thrift.CassandraDaemon’ process.

Java clientèle

Well, there are two serious contenders, Pelops and Hector. Both have released experimental support for Version 0.7. I had worked with Pelops earlier, so I thought this is time to give Hector a chance.

  • Download Hector (Sync release with Cassandra 0.7.0-beta2) from here: http://github.com/rantav/hector/downloads
    You can also use ‘git clone‘ to download the latest source.
  • Hector is a maven project. To compile the source into ‘jar’, just extract the release and run,
    > mvn package

My first program

To start with Hector, I thought to write a very small code to insert a Column and then later fetch it back. If you remember, in the previous section, we already created a keyspace ‘Keyspace1‘ and a Column Family ‘Standard1‘, and not we are going to make use of them.

import me.prettyprint.cassandra.serializers.StringSerializer;
import me.prettyprint.hector.api.Cluster;
import me.prettyprint.hector.api.Keyspace;
import me.prettyprint.hector.api.beans.HColumn;
import me.prettyprint.hector.api.exceptions.HectorException;
import me.prettyprint.hector.api.factory.HFactory;
import me.prettyprint.hector.api.mutation.Mutator;
import me.prettyprint.hector.api.query.ColumnQuery;
import me.prettyprint.hector.api.query.QueryResult;

public class HectorFirstExample {

	public static void main(String[] args) throws Exception {

		String keyspaceName = "Keyspace1";
		String columnFamilyName = "Standard1";
		String serverAddress = "localhost:9160";

		// Create Cassandra cluster
		Cluster cluster = HFactory.getOrCreateCluster("Cluster-Name", serverAddress);
		// Create Keyspace
		Keyspace keyspace = HFactory.createKeyspace(keyspaceName, cluster);

		try {
			// Mutation
			Mutator mutator = HFactory.createMutator(keyspace, StringSerializer.get());
			// Insert a new column with row-id 'id-1'
			mutator.insert("id-1", columnFamilyName, HFactory.createStringColumn("Animesh", "Kumar"));

			// Look up the same column
			ColumnQuery columnQuery = HFactory.createStringColumnQuery(keyspace);
			columnQuery.setColumnFamily(columnFamilyName).setKey("id-1").setName("Animesh");
			QueryResult> result = columnQuery.execute();

			System.out.println("Read HColumn from cassandra: " + result.get());
		} catch (HectorException e) {
			e.printStackTrace();
		}
	}
}

That was simple. By the way, ‘Nate McCall‘ has written a set of example classes to help us understand Hector with Cassandra 0.7. Check it out here: http://github.com/zznate/hector-examples

I am working towards introducing Cassandra 0.7 support in Kundera, and will be publishing my findings intermittently.

Written by Animesh

October 14, 2010 at 9:26 pm

Posted in Technology

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Kundera: now JPA 1.0 Compatible

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If you are new to Kundera, you should read Kundera: knight in the shining armor! to get a brief idea about it.

Kundera has reached a major milestone lately, so I thought to sum up the developments here. First and foremost, Kundera is now JPA 1.0 compatible, thought it doesn’t support relationships yet, it does support easy JPA style @Entity declarations and Linear JPA Queries. 🙂 Didn’t you always want to search over Cassandra?

To begin with let’s see what the changes are.

  1. Kundera do not have @CassandraEntity annotation anymore. It now expects JPA @Entity.
  2. Kundera specific @Id has been replaced with JPA @Id.
  3. Kundera specific @Column has been replaced with JPA @Column.
  4. @ColumnFamily, @SuperColumnFamily and @SuperColumn are still there, and are expected to be there for a long time to come, because JPA doesn’t have any of these ideas.
  5. @Index is introduced to control indexing of an entity bean. You can safely ignore it and let Kundera do the defaults for you.

I would recommend you to read about Entity annotation rules discussed in the earlier post. Apart from the points mentioned above, everything remains the same:  https://anismiles.wordpress.com/2010/06/30/kundera-knight-in-the-shining-armor/#general-rules

How to define an entity class?

@Entity						// makes it an entity class
@ColumnFamily("Authors")	// assign ColumnFamily type and name
public class Author {

	@Id	// row identifier
	String username;

	@Column(name = "email")	// override column-name
	String emailAddress;

	@Column
	String country;

	@Column(name = "registeredSince")
	Date registered;

	String name;

	public Author() { // must have a default constructor
	}

	// getters, setters etc.
}

There is an important deviation from JPA specification here.

  1. Unlike JPA you must explicitly annotate fields/properties you want to persist. Any field/property that is not @Column annotated will be ignored by Kundera.
  2. In short, the paradigm is reversed here. JPA assumes everything persist-able unless explicitly defined @Transient. Kundera expects everything transient unless explicitly defined @Column.

How to instantiate EntityManager?

Kundera expects some properties to be provided with before you can bootstrap it.

# kundera.properties
# Cassandra nodes to with Kundera will connect
kundera.nodes=localhost

#Cassandra port
kundera.port=9160

#Cassandra keyspace which Kundera will use
kundera.keyspace=Blog

#Whether or not EntityManager can have sessions, that is L1 cache.
sessionless=false

#Cassandra client implementation. It must implement com.impetus.kundera.CassandraClient
kundera.client=com.impetus.kundera.client.PelopsClient

You can define these properties in a java Map object, or in JPA persistence.xml or in a property file “kundera.properties” kept in the classpath.

  1. Instantiating with persistence.xml > Just replace the provider with com.impetus.kundera.ejb.KunderaPersistence which extends JPA PersistenceProvider. And either provide Kundera specific properties in the xml file or keep “kundera.properties” in the classpath.
  2. Instantiating in standard J2SE environment, with explicit Map object.
    Map map = new HashMap();
    map.put("kundera.nodes", "localhost");
    map.put("kundera.port", "9160");
    map.put("kundera.keyspace", "Blog");
    map.put("sessionless", "false");
    map.put("kundera.client", "com.impetus.kundera.client.PelopsClient");
    
    EntityManagerFactory factory = new EntityManagerFactoryImpl("test", map);
    EntityManager manager = factory.createEntityManager();
    
  3. Instantiating in standard J2SE environment, with “Kundera.properties” file. Pass null to EntityManagerFactoryImpl and it will automatically look for the property file.
    EntityManagerFactory factory = new EntityManagerFactoryImpl("test", null);
    EntityManager manager = factory.createEntityManager();
    

Entity Operations

Once you have EntityManager object you are good to go, applying all your JPA skills. For example, if you want to find an Entity object by key,

	try {
		Author author = manager.find(Author.class, "smile.animesh");
	} catch (PersistenceException pe) {
		pe.printStackTrace();
	}

Similarly, there are other JPA methods for various operations: merge, remove etc.

JPA Query

Note: Kundera uses Lucene to index your Entities. Beneath Lucene, Kundera uses Lucandra to store the indexes in Cassandra itself. One fun implication of using Lucene is that apart from regular JPA queries, you can also run Lucene queries. 😉

Here are some indexing fundamentals:

  1. By default, all entities are indexed along with with all @Column properties.
  2. If you do not want to index an entity, annotate it like, @Index (index=false)
  3. If you do not want to index a @column property of an entity, annotate it like, @Index (index=false)

That’s it. Here is an example of JPA query:

	// write a JPA Query
	String jpaQuery = "SELECT a from Author a";

	// create Query object
	Query query = manager.createQuery(jpaQuery);

	// get results
	List<Author> list = query.getResultList();
	for (Author a : list) {
		System.out.println(a.getUsername());
	}

Kundera also supports multiple “where” clauses with “AND”, “OR”, “=” and “like” operations.

	// find all Autors with email like anismiles
	String jpaQuery_for_emails_like = "SELECT a from Author a WHERE a.emailAddress like anismiles";

	// find all Authors with email like anismiles or username like anim
	String jpaQuery_for_email_or_name = "SELECT a from Author a WHERE a.emailAddress like anismiles OR a.username like anim";

I think this will enable you to play around with Kundera. I will be writing up more on how Kundera indexes various entities and how you can execute Lucene Queries in subsequent posts.

Kundera’s next milestones will be:

  1. Implementation of JPA listeners, @PrePersist @PostPersist etc.
  2. Implementation of Relationships, @OneToMany, @ManyToMany etc.
  3. Implementation of Transactional support, @Transactional

Written by Animesh

July 14, 2010 at 9:51 am

Posted in Technology

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Kundera: knight in the shining armor!

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The idea behind Kundera is to make working with Cassandra drop-dead simple, and fun. Kundera does not reinvent the wheel by making another client library; rather it leverages the existing libraries, and builds – on top of them – a wrap-around API to developers do away with the unnecessary boiler plate codes, and program  a neater, cleaner code that reduces code-complexity and improves quality. And above all, improves productivity.

Download Kundera here: http://code.google.com/p/kundera/

Note: Kundera is now JPA 1.0 compatible, and there are some ensuing changes. You should read about it here: https://anismiles.wordpress.com/2010/07/14/kundera-now-jpa-1-0-compatible/

Objectives:

  • To completely remove unnecessary details, such as Column lists, SuperColumn lists, byte arrays, Data encoding etc.
  • To be able to work directly with Domain models just with the help of annotations
  • To eliminate “code plumbing”, so as to keep the flow of data processing clear and obvious
  • To completely separate out Cassandra and its obvious concerns from application-level logics for robust application development
  • To include the latest Cassandra developments without breaking anything, anywhere in the business layer

Cassandra Data Models

At the very basic level, Cassandra has Column and SuperColumn to hold your data. Column is a tuple with a name, value and a timestamp; while SuperColumn is Column of Columns. Columns are stored in a ColumnFamily, and SuperColumns in SuperColumnFamily. The most important thing to note is that Cassandra is not your old relational database, it is a flat system. No joins, No foreign keys, nothing. Everything you store here is 100% de-normalized.

Read more details here: https://anismiles.wordpress.com/2010/05/18/cassandra-data-model/

Using Kundera

Kundera defines a range of annotations to describe your Entity objects. Kundera is now JPA1.0 compatible. It builds a range of various Annotations, on top of JPA annotations, to suit its needs. Here are the basic rules:

General Rules

  • Entity classes must have a default no-argument constructor.
  • Entity classes must be annotated with @CassandraEntity @Entity (@CassandraEntity annotation is dropped in favor of JPA @Entity)
  • Entity classes for ColumnFamily must be annotated with @ColumnFamily(“column-family-name”)
  • Entity classes for SuperColumnFamily must be annotated with @SuperColumnFamily(“super-column-family-name”)
  • Each entity must have a field annotation with @Id
    • @Id field must of String type. (Since you can define sorting strategies in Cassandra’s storage-conf file, keeping @Id of String type makes life simpler, you will see later)
    • There must be 1 and only 1 @Id per entity.

Note: Kundera works only at property level for now, so all method level annotations are ignored. Idea: keep life simple. 🙂

ColumnFamily Rules

  1. You must define the name of the column family in @ColumnFamily, like @ColumnFamily (“Authors”) Kundera will link this entity class with “Authors” column family.
  2. Entities annotated with @ColumnFamily are scanned for properties for @Colum annotations.
  3. Each such field will qualify to become a Cassandra Column with
    1. Name: name of the property.
    2. Value: value of the property
  4. By default the name of the column will be the name of the property. However, you fancy changing the name, you can override it like, @Column (name=”fancy-name”)
    @Column (name="email")          // override column-name
    String emailAddress;
    
  5. Properties of type Integer, String, Long and Date are inherently supported, rest all will be serialized before they get saved, and de-serialized while getting read. Serialization has some inherent limitations; that is why Kundera discourages you to use custom objects as Cassandra Column properties. However, you are free to do as you want. Just read the serialization tweaks before insanity reins over you, 😉
  6. Kundera also supports Collection and Map properties. However there are few things you must take care of:
    • You must initialize any Collection or Map properties, like
      List<String> list = new ArrayList<String>();
      Set<String> set = new HashSet<String>();
      Map<String, String> map = new HashMap<String, String>();
      
    • Type parameters follow the same rule, described in #5.
    • If you don’t explicitly define the type parameter, elements will be serialized/de-serialized before saving and retrieving.
    • There is no guarantee that the Collection element order will be maintained.
    • Collection and Map both will create as many columns as the number of elements it has.
    • Collection will break into Columns  like,
      1. Name~0: Element at index 0
      2. Name~1: Element at index 1 and so on.

      Name follows rule #4.

    • Map will break into Columns like,
      1. Name~key1: Element at key1
      2. Name~key2: Element at key2 and so on.
    • Again, name follows rule #4.

SuperColumnFamily Rules

  1. You must define the name of the super column family in @SuperColumnFamily, like @SuperColumnFamily (“Posts”) Kundera will link this entity class with “Posts” column family.
  2. Entities annotated with @SuperColumnFamily are scanned for properties for 2 annotations:
    1. @Column and
    2. @SuperColumn
  3. Only properties annotated with both annotations are picked up, and each such property qualifies to become a Column and fall under SuperColumn.
  4. You can define the name of the column like you did for ColumnFamily.
  5. However, you must define the name of the SuperColumn a particular Column must fall under like, @SuperColumn(column = “super-column-name”)
    @Column
    @SuperColumn(column = "post")  // column 'title' will fall under super-column 'post'
    String title;
    
  6. Rest of the things are same as above.

Up and running in 5 minutes

Let’s learn by example. We will create a simple Blog application. We will have Posts, Tags and Authors.

Cassandra data model for “Authors” might be like,

ColumnFamily: Authors = {
    “Eric Long”:{		// row 1
        “email”:{
            name:“email”,
            value:“eric (at) long.com”
        },
        “country”:{
            name:“country”,
            value:“United Kingdom”
        },
        “registeredSince”:{
            name:“registeredSince”,
            value:“01/01/2002”
        }
    },
    ...
}

And data model for “Posts” might be like,

SuperColumnFamily: Posts = {
	“cats-are-funny-animals”:{		// row 1
		“post” :{		// super-column
			“title”:{
				“Cats are funny animals”
			},
			“body”:{
				“Bla bla bla… long story…”
			}
			“author”:{
				“Ronald Mathies”
			}
			“created”:{
				“01/02/2010"
			}
		},
		“tags” :{
			“0”:{
				“cats”
			}
			“1”:{
				“animals”
			}
		}
	},
	// row 2
}

Create a new Cassandra Keyspace: “Blog”

<Keyspace Name="Blog">
<!—family definitions-->

<!-- Necessary for Cassandra -->
<ReplicaPlacementStrategy>org.apache.cassandra.locator.RackUnawareStrategy</ReplicaPlacementStrategy>
<ReplicationFactor>1</ReplicationFactor>
<EndPointSnitch>org.apache.cassandra.locator.EndPointSnitch</EndPointSnitch>
</Keyspace>

Create 2 column families: SuperColumnFamily for “Posts” and ColumnFamily for “Authors”

<Keyspace Name="Blog">
<!—family definitions-->
<ColumnFamily CompareWith="UTF8Type" Name="Authors"/>
<ColumnFamily ColumnType="Super" CompareWith="UTF8Type" CompareSubcolumnsWith="UTF8Type" Name="Posts"/>

<!-- Necessary for Cassandra -->
<ReplicaPlacementStrategy>org.apache.cassandra.locator.RackUnawareStrategy</ReplicaPlacementStrategy>
<ReplicationFactor>1</ReplicationFactor>
<EndPointSnitch>org.apache.cassandra.locator.EndPointSnitch</EndPointSnitch>
</Keyspace>

Create entity classes

Author.java

@Entity			// makes it an entity class
@ColumnFamily ("Authors")	// assign ColumnFamily type and name
public class Author {

    @Id						// row identifier
    String username;

    @Column (name="email")	// override column-name
    String emailAddress;

    @Column
    String country;

    @Column (name="registeredSince")
    Date registered;

    String name;

    public Author () {		// must have a default constructor
    }

    ... // getters/setters etc.
}

Post.java

@Entity					// makes it an entity class
@SuperColumnFamily("Posts")			// assign column-family type and name
public class Post {

	@Id								// row identifier
	String permalink;

	@Column
	@SuperColumn(column = "post")	// column 'title' will be stored under super-column 'post'
	String title;

	@Column
	@SuperColumn(column = "post")
	String body;

	@Column
	@SuperColumn(column = "post")
	String author;

	@Column
	@SuperColumn(column = "post")
	Date created;

	@Column
	@SuperColumn(column = "tags")	// column 'tag' will be stored under super-column 'tags'
	List<String> tags = new ArrayList<String>();

	public Post () {		// must have a default constructor
	}

       ... // getters/setters etc.
}

Note the annotations, match them against the rules described above. Please see how “tags” property has been initialized. This becomes very important because Kundera uses Java Reflection to read and populate the entity classes. Anyways, once we have entity classes in place…

Instantiate EnityManager

Kundera now works as a JPA provider, and here is how you can instantiate EntityManager. https://anismiles.wordpress.com/2010/07/14/kundera-now-jpa-1-0-compatible/#entity-manager

EntityManager manager = new EntityManagerImpl();
manager.setClient(new PelopsClient());
manager.getClient().setKeySpace("Blog");

And that’s about it. You are ready to rock-and-roll like a football. Sorry, I just got swayed with FIFA fever. 😉

Supported Operations

Kundera supports JPA EntityManager based operations, along with JPA queries. Read more here: https://anismiles.wordpress.com/2010/07/14/kundera-now-jpa-1-0-compatible/#entity-operations


Save entities

Post post = ... // new post object
try {
manager.save(post);
} catch (IllegalEntityException e) { e.printStackTrace(); }
catch (EntityNotFoundException e) { e.printStackTrace(); }

If the entity is already saved in Cassandra database, it will be updated, else a new entity will be saved.
Load entity

try {
Post post = manager.load(Post.class, key); // key is the identifier, for our case, "permalink"
} catch (IllegalEntityException e) { e.printStackTrace(); }
catch (EntityNotFoundException e) { e.printStackTrace(); }

Load multiple entities

try {
List posts = manager.load(Post.class, key1, key2, key3...); // key is the identifier, "permalink"
} catch (IllegalEntityException e) { e.printStackTrace(); }
catch (EntityNotFoundException e) { e.printStackTrace(); }

Delete entity

try {
manager.delete(Post.class, key); // key is the identifier, "permalink"
} catch (IllegalEntityException e) { e.printStackTrace(); }
catch (EntityNotFoundException e) { e.printStackTrace(); }


Wow! Was it fun? Was it easy? I’m sure it was. Keep an eye on Kundera, we will be rolling out sooner-than-you-imagine more features like,

  1. Transaction support
  2. More fine-grained methods for better control
  3. Lazy-Loading/Selective-Loading of entity properties and many more.

Written by Animesh

June 30, 2010 at 7:12 pm

Posted in Technology

Tagged with , , , ,

Lucandra – an inside story!

with 14 comments

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Lucene works with

  1. Index,
  2. Document,
  3. Field and
  4. Term.

An index contains a sequence of documents. A document is a sequence of fields. A field is a named sequence of terms. A term is a string that represents a word from text. This is the unit of search. It is composed of two elements, the text of the word, as a string, and the name of the field that the text occured in, an interned string. Note that terms may represent more than words from text fields, but also things like dates, email addresses, urls, etc.

Lucene’s index is inverted index. In normal indexes, you can look for a document to know what fields it contains. In inverted index, you look for a field to know all other documents it appears in. It’s kind of upside-down view of the world. But it makes searching blazingly fast.

Read More: http://lucene.apache.org/java/3_0_1/fileformats.html

On a very high level, you can think of lucene indexes as 2 buckets:

  1. Bucket-1 keeps all the Terms (with additional info like, term frequency, position etc.) and it knows which documents have these terms.
  2. Bucket-2 stores all leftover field info, majorly non-indexed info.

How Lucandra does it?

Lucandra needs 2 column families for each bucket described above.

  1. Family-1 to store Term info. We call it “TermInfo”
  2. Family-2 to store leftover info. We call it “Documents”

“TermInfo” family is a SuperColumnFamily. Each term gets stored in a separate row identified with TermKey (“index_name/field/term”) and stores SuperColumns containing Columns of various term information like, term frequency, position, offset, norms etc. This is how it looks:

"TermInfo" => {
    TermKey1:{                                        // Row 1
        docId:{
            name:docId,
            value:{
                Frequencies:{
                    name: Frequencies,
                    value: Byte[] of List[Number]
                },
                Position:{
                    name: Position,
                    value: Byte[] of List[Number]
                },
                Offsets:{
                    name: Offsets,
                    value: Byte[] of List[Number]
                },
                Norms:{
                    name: Norms,
                    value: Byte[] of List[Number]
                }
            }
        }
    },
    TermKey2 => {                                    // Row 2
    }
    ...
}

“Documents” family is a StandardColumnFamily. Each document gets stored in a separate row identified with DocId (“index_name/document_id”) and stores Columns of various storable fields. This looks like,

"Documents" => {
        DocId1: {                        // Row 1
            field1:{
                name: field1,
                value: binary storable content
            },
            field2{
                name: field2,
                value: binary storable content
            }
        },
        DocId2: {                        // Row 2
            field1:{
                name: field1,
                value: binary storable content
            },
        ...
        },
        ...
    }

The Lucandra Cassandra Keyspace looks like this:

<Keyspace Name="Lucandra">
    <ColumnFamily Name="TermInfo"
        CompareWith="BytesType"
        ColumnType="Super"
        CompareSubcolumnsWith="BytesType"
        KeysCached="10%" />
    <ColumnFamily Name="Documents"
        CompareWith="BytesType"
        KeysCached="10%" />

    <ReplicaPlacementStrategy>
        org.apache.cassandra.locator.RackUnawareStrategy
    </ReplicaPlacementStrategy>
    <ReplicationFactor>1</ReplicationFactor>
    <EndPointSnitch>
        org.apache.cassandra.locator.EndPointSnitch
    </EndPointSnitch>
</Keyspace>

Lucene has got many powerful features, like wildcards queries, result sorting, range queries etc. For Lucandra to have these features enabled, you must configure Cassandra with OrderedPreservingParitioner, i.e. OPP.

Cassandra comes with RandomPartitioner, i.e. RP by default, but

  1. RP does NOT support Range Slices, and
  2. If you scan through your keys, they will NOT come in order.

If you still insist on using RP, you might encounter some exceptions, and you might need to go to Lucandra source to amend range query sections.

java.lang.RuntimeException: InvalidRequestException(why:start key's md5 sorts after
end key's md5.this is not allowed; you probably should not specify end key at all,
under RandomPartitioner)
    at lucandra.LucandraTermEnum.loadTerms(LucandraTermEnum.java:217)
    at lucandra.LucandraTermEnum.skipTo(LucandraTermEnum.java:88)
    at lucandra.IndexReader.docFreq(IndexReader.java:163)
    at org.apache.lucene.search.IndexSearcher.docFreq(IndexSearcher.java:138)

This is what you need to change in Cassandra config:

<Partitioner>org.apache.cassandra.dht.OrderPreservingPartitioner</Partitioner>

Benefits

  1. Since you can pull ranges of keys and groups of columns in Cassandra, you can really tune the performance of reads and minimize network IO for each query.
  2. Since writes are indexed in Cassandra, and Cassandra replicates itself, you don’t need to worry about optimizing the indexes or reopening the index to see new writes. With Lucene you need to take care of optimizing your indexes from time to time, and you need to re-instantiate your Searcher object to see new writes.
  3. So, with Cassandra underlying Lucene, you get a real-time distributed search engine.

Caveats

As we discussed in earlier post, you can extend Lucene either by implementing you own Directory class, or writing your own IndexReader and IndexWriter classes. And Lucandra does it using the former approach and it makes much more sense.

Read here: Apache Lucene and Cassandra

Benefits that Lucandra gets are because of Cassandra’s amazing capability to store and scale the key-value pairs. Directory class works in close proximity with IndexReader and IndexWriter to store and read indexes from some storage (filesystem and/or database). It generally receives huge chunks of sequential bytes, not a key-value pair, which would be difficult to store in Cassandra, and even if stored, it would not make optimum use of Cassandra.

Anyhow, given that Lucene is not very object oriented and almost never uses interfaces, using Lucandra’s IndexWriter and IndexReader seamlessly with your legacy codes will NOT be possible.

Lucandra’s IndexReader extends org.apache.lucene.index.IndexReader which makes this class fit for your legacy codes. You just need to instantiate it and then you can pass it around to your native code without much thought:

IndexReader indexReader = new IndexReader(INDEX_NAME, cassandraClient);
// Notice that the constructor is different.
IndexSearcher indexSearcher = new IndexSearcher(indexReader);

But mind you, Lucandra’s IndexReader will NOT help you walk through the indexed documents. Who needs it anyway? 😉

However, Lucandra’s IndexWriter is an independent class, and doesn’t extend or relates to org.apache.lucene.index.IndexWriter in any way. That makes it impossible to use this class in your legacy codes without re-factoring. But, to ease you pain, it does implement the methods with the same signature as native’s, e.g. addDocument, deleteDocuments etc. have the same signature. If that makes you a little happy. 🙂

Also, Lucandra attempts to re-write all related logic inside its IndexWriter, for example, logic to invoke analyzer to fetch terms, calculating term frequencies, offsets etc. This too makes Lucandra a bit weird for future portability. Whenever, Lucene introduces a new thing, or changes its logic in any way, Lucanadra will need to re-implement them. For example, Lucene recently introduced Payloads which add weights to specific terms, much like spans. It works by extending Similarity class with additional logic. Lucandra doesn’t support it. And to support, Lucandra would need to amend its code.

In short, I am trying to say that the way Lucandra is implemented it would make it difficult to inherently use any future Lucene enhancements, but – God forbid! – there is no other way around. Wish Lucene had a better structure!

Anyways, right now, Lucandra supports:

  1. Real-Time indexing
  2. Zero optimization
  3. Search
  4. Sort
  5. Range Queries
  6. Delete
  7. Wildcards and other Lucene magic
  8. Faceting/Highlighting

Apart from this, the way Lucandra uses Cassandra can also have some scalability issues with large data. You can find some clue here:
http://ria101.wordpress.com/2010/02/22/cassandra-randompartitioner-vs-orderpreservingpartitioner/

Performance

Lucandra claims that it’s slower that Lucene. Indexing is ~10% slower, and so is reading. However, I found it must better and faster than Lucene. I wrote comparative tests to index 15K documents, and search over the index. I ran the tests on my Dell-Latitude D520 with 3GB RAM, and Lucandra (single Cassandra node) was ~35% faster than Lucene during indexing, and ~20% for search. May be, I should try with bigger set of data.

is Lucandra production ready?

There is a Twitter search app http://sparse.ly which is built on Lucandra. This service uses Lucandra exclusively, without any relational or other sort of databases. Given the depth and breadth of twitter data, and that sparse.ly is pretty popular and stable, Lucandra does seem to be production ready.

🙂 But, may be, you should read the Caveats once more and see if you are okay with them.

Written by Animesh

May 27, 2010 at 8:03 am

Posted in Technology

Tagged with , , , ,

Connecting to Cassandra – 1

with 13 comments

Cassandra uses the Apache Thrift framework as its client API. Apache Thrift is a remote procedure call framework “scalable cross-language services development”. You can define data types and service interfaces in a thrift definition file, through which the compiler generates the code in your chosen languages. Effectively, it combines a software stack with a code generation engine to build services that work efficiently and seamlessly between a numbers of languages.

Apache Thrift – though is a state of art engineering feat – is not the best choice for a client API, especially for Cassandra.

  1. Cassandra supports multiple nodes, and you can connect to any node anytime. And this is an amazing thing, because if a node falls down, a client can connect to any other node available without pulling system down. Alas! Apache Thrift doesn’t support this inherently, you need to make you client aware of node-failures and write a strategy to pick up a next alive node.
  2. Thrift doesn’t support connection pooling. So, either you connect to the server every time, or keep a connection alive for a longer period of time. Or, perhaps, write a connection pool engine. Sad!

There are few clients available which make these things easier for you. They are like wrapper over Thrift to save you from a lot of nuisance. Anyhow, since even those clients work on top of Thrift, it makes sense to learn Thrift: to make our foundation strong.

Let’s first create a dummy Keyspace for ourselves:

<Keyspace Name="AddressBook">
<ColumnFamily CompareWith="UTF8Type" Name="Users" />

<!-- Necessary for Cassandra -->
<ReplicaPlacementStrategy>org.apache.cassandra.locator.RackUnawareStrategy
</ReplicaPlacementStrategy>
<ReplicationFactor>1</ReplicationFactor>
<EndPointSnitch>org.apache.cassandra.locator.EndPointSnitch</EndPointSnitch>
</Keyspace>

We created a new Keyspace “AddressBook” which has a ColumnFamily “Users” with sorting policy of “UTF8Type” type.

Connect to Cassandra Server:

private TTransport transport = null;
private Cassandra.Client client = null;

public Cassandra.Client connect(String host, int port) {
    try {
        transport = new TSocket(host, port);
        TProtocol protocol = new TBinaryProtocol(transport);
        Cassandra.Client client = new Cassandra.Client(protocol);
        transport.open();
        return client;
    } catch (TTransportException e) {
        e.printStackTrace();
    }
    return null;
}

The above code is pretty fundamental:

  1. Opens up a Socket at the given host and port.
  2. Defines a protocol, in this case, it’s binary.
  3. And instantiates the client object.
  4. Returns client object for further operations.

Note: Cassandra uses “9160” as its default port.

Disconnect from Cassandra Server:

public void disconnect() {
    try {
        if (null != transport) {
            transport.flush();
            transport.close();
        }
    } catch (TTransportException e) {
        e.printStackTrace();
    }
}

To close the connection in a descent way, you should invoke “flush” to take care of any data that might still be there in the transport buffer.

Store a data object:

Let’s say, our User object is something like below:

public class User {
    // unique
    private String username;
    private String email;
    private String phone;
    private String zip;

    // getter and setter here.
}

To model one User to Cassandra, we need 3 columns to store email, phone and zip and the name of the row would be username. Right? Let’s create a list to store these columns.

List<ColumnOrSuperColumn> columns = new ArrayList<ColumnOrSuperColumn>();

The List contains ColumnOrSuperColumn objects. Cassandra gives us an aggregate object which can contain either a Column or a SuperColumn. You wonder why? Because, Apache thrift doesn’t support inheritance. Anyways, now we will create columns and store them in this list.

// generate a timestamp.
long timestamp = new Date().getTime();
ColumnOrSuperColumn c = null;

// add email
c = new ColumnOrSuperColumn();
c.setColumn(new Column("email".getBytes("utf-8"), user.getEmail().getBytes("utf-8"), timestamp));
columns.add(c);

// add phone
c = new ColumnOrSuperColumn();
c.setColumn(new Column("phone".getBytes("utf-8"), user.getPhone().getBytes("utf-8"), timestamp));
columns.add(c);

// add zip
c = new ColumnOrSuperColumn();
c.setColumn(new Column("zip".getBytes("utf-8"), user.getZip().getBytes("utf-8"), timestamp));
columns.add(c);

Okay, so we have the list of columns populated. Now, we need a Map which will hold the rows, that is list of columns. Key to this map will be the name of the ColumnFamily.


Map<String, List<ColumnOrSuperColumn>> data = new HashMap<String, List<ColumnOrSuperColumn>>();
data.put("Users", columns); // “Users” is our ColumnFamily Name.

Great. We have everything in place. Now, we will use client.batch_insert to store everything at once. This will create row in the ColumnFamily identified by the given key.


client.batch_insert( "AddressBook",          // Keyspace
                      user.getUsername(),    // Row identifier key
                      data,                  // Map which contains the list of columns.
                      ConsistencyLevel.ANY   // Consistency level. Explained below.
);

ConsistencyLevel parameter is used for both read and write operations to determine when the request made by the client is successful. ConsistencyLevel.ANY means that a write action is successful when it has been written to at least one node. Read Cassandra Wiki for a detailed information.

In the next blog, we will see how to delete and update a record in Casandra.

Written by Animesh

May 24, 2010 at 10:42 am

Posted in Technology

Tagged with , , ,

Apache Lucene and Cassandra

with 5 comments

[tweetmeme source=”anismiles” only_single=false http://www.URL.com%5D

I am trying to find ways to extend and scale Lucene to use various latest data storing mechanisms, like Cassandra, simpleDB etc. Why? Agree that Lucene is wonderful, blazingly high-performance with features like incremental indexing and all. But managing and scaling storage, reads, writes and index-optimizations sucks big time. Though we have Solr, Jboss’ Infinispan, and Berkeley’s DbDirectory etc. but the approach they have adopted is very conventional and do not leverage upon any of latest technological developments in non-relational, highly scalable and available data stores like Cassandra, couchDB etc.

And then, I came across Lucandra: an attempt to use Cassandra as an underlying data storage mechanism for Lucene. Ain’t the name(Lucene + Cassandra) say so? 🙂

Why Cassandra?

  1. Well, Cassandra is one of the most popular and widely used “NoSql” systems.
  2. Flexible: Cassandra is a scalable and easy to administer column-oriented data store. Read and write throughput both increase linearly as new machines are added, with no downtime or interruption to applications.
  3. Decentralized: Cassandra does not rely on a global file system, but uses decentralized peer to peer “Gossip”, and so, it has no single point of failure, and introducing new nodes to the cluster is dead simple.
  4. Fault-Tolerant: Cassandra also has built-in multi-master write, replication, rack awareness, and can handle dead nodes gracefully.
  5. Highly Available: Writes and reads offer a tunable ConsistencyLevel, all the way from “writes never fail” to “block for all replicas to be readable,” with the quorum level in the middle.
  6. And, Cassandra has a thriving community and is at production for products like Facebook, Digg, Twitter etc.

Cool. The idea sounds awesome. But wait, before we look into how Lucandra actually implements it, let’s try to find what are the possible ways of implementation. We need to understand the Lucene stack first, and where and how it can be extended?

Lucene Stack

There are 3 elementary components, IndexReader, IndexWriter and Directory. IndexWriter writes reverse indexes of a document with the help of Directory implementation to a disk. IndexReader reads from the indexes using the same Directory.

But, there is a catch. Lucene is not very well designed and its APIs are closed.

  1. Very poor OO design. There are classes, packages but almost no design pattern usage.
  2. Almost no use of interfaces. Query, HitCollector etc. are all subclasses of an abstract class, so:
    1. You’ll have to constantly cast your custom query objects to a Query in order to be able to use your objects in native Lucene calls.
    2. It’s pain to apply AOP and auto-proxying.
  3. Some classes which should have been inner are not, and anonymous classes are used for complex operations where you would typically need to override their behavior.

There are many more. Point is that Lucene is designed in such a way that you will upset your code purity no matter how you do it.

Read more:
http://www.jroller.com/melix/entry/why_lucene_isn_t_that
http://lucene.grantingersoll.com/2008/03/28/why-lucene-isnt-that-good-javalobby/

Anyhow, to extend Lucene, there are 2 approaches:

  1. Either write a custom Directory implementation, or
  2. write custom IndexReader and IndexWriter classes.

Incorporating Cassandra by writing a custom Directory

This involves extending abstract Directory class. There are many examples like Lucene Jdbc Directory, Berkeley’s DbDirectory etc. for consultation.

Incorporating Cassandra by writing custom IndexReader and IndexWriter

This is a crude approach: writing custom IndexReader and IndexWriter classes. Note again, that native Lucene’s reader/writer classes don’t implement any Interfaces and hence it will be difficult to plug and use our custom reader/writer classes in any existing code. Well, but that’s what you get. Another thing is that, native IndexReader/IndexWriter classes perform a lot of additional logic than just indexing and reading. They use analyzers to analyze the supplied document, calculate terms, term frequencies to name few. We need to make sure that we don’t miss any of these lest Lucene shouldn’t do what we expect it to do.

Lucandra follows this approach. It has written a custom IndexWriter and IndexReader classes. I am going to explore more on it, and come back with what I find there.

Read it here: Lucandra – an inside story!

Trivia

Do you know where does the name Lucene come from? Lucene is Doug Cutting‘s wife’s middle name, and her maternal grandmother’s first name. Lucene is a common Armenian first name.

And, what about Cassandra? In Greek mythology the name Cassandra means “Inflaming Men with Love” or an unheeded prophetess. She is a figure both of the epic tradition and of tragedy. Remember the movie Troy? Although, the movie was a not exactly what Odysseus wrote, but it was polluted to create more appealing cinematic drama. Read here: http://ancienthistory.about.com/cs/grecoromanmyth1/a/troymoviereview.htm

Written by Animesh

May 19, 2010 at 7:26 am

Posted in Technology

Tagged with , , , , ,

Cassandra – Data Model

with 7 comments

[tweetmeme source=”anismiles” only_single=false http://www.URL.com%5D

Cassandra is a completely different concept for a database. And if you are coming from the RDBMS background, you sure are going to have a tough time understanding the fundamentals, for example, you won’t find anything like tables, columns, constraints, indexes, queries etc. at least not in the since sense of relational databases. Cassandra has an altogether different approach toward DataModels.

Column

The column is the lowest/smallest data container in Cassandra. It’s a tuple (triplet) that contains a name, a value and a timestamp.

user: {
    name: user,
    value: animesh.kumar,
    timestamp: 98989L
}

Name and Value are both binary (technically byte[]) and can be of any length.

SuperColumn

To understand SuperColumn, try to look at it as a tuple which instead of containing binary values, contains a Map of unbounded Columns.

homeAddress: {
    name: homeAddress,
    value: {
        street: {
            name:street,
            value:MG-101,
            timestamp: 98989L
        },
        city: {
            name:city,
            value:Indore,
            timestamp: 98989L
        },
        landmark:{
            name:landmark,
            value:Bombay Hospital,
            timestamp: 98989L
        },
        zip:{
            name:zip,
            value:452001,
            timestamp: 98989L
        }
    }
}

SuperColumn can be summarized as a column of Columns. It has got a Map styled containers to hold unbounded number of Columns (key has to be the same as the name of the Column). Also notice that SuperColumns don’t have a timestamp component.

Grouping

Now, since we got two elementary DataModels, i.e. Column and SuperColumn, we need some mechanisms to hold them together, or group them.

Column Family

ColumnFamily is a structure that can keep an infinite number of rows – for most people with an RDBMS background – which is the structure that resembles a Table the most.

A ColumnFamily has:

  1. A name (think of the name of a Table),
  2. A map with a key (Row Identifier, like Primary Key) and
  3. A value which is a Map containing Columns.

For the Map with the columns: the key has to be the same as the name of the Column.

Profile = {
    smile.animesh:{
        username:{
            name:username,
            value:smile.animesh
        },
        email:{
            name:email,
            value:smile.animesh@gmail.com
        },
        age:{
            name:age,
            value:25
        }
    },
    ashish:{
    ...
    },
    santosh:{
    ...
    }
}

SuperColumn Family

SuperColumnFamily is easy after you have gotten through ColumnFamily. Instead of having Columns in the inner most Map we have SuperColumns. So it just adds an extra dimension.

AddressBook = {
    smile.animesh: {  // 1st row.
        ashish: {
            name:ashish,
            value:{
                street:{
                    name:street,
                    value:Dhurwa,
                },
                zip:{
                    name:zip,
                    value:9898
                },
                city:{
                    name:city,
                    value:Indore
                }
        },
        papa: {
            name:papa,
            value:{
                street:{
                    name:street,
                    value:Rajwada,
                },
                zip:{
                    name:zip,
                    value:83400
                },
                city:{
                    name:city,
                    value:Ranchi
                }
        },
    }, // end row
    ashish: {     // 2nd row.
    ...
    },
}

Keyspaces

A Keyspace is the outer most grouping of the data. From an RDBMS point of view you can compare this to your database schema, normally you have one per application.

A Keyspace contains the ColumnFamilies. There is no relationship between the ColumnFamiliies. They are just separate containers. They are NOT like tables in MySQL – you can’t join them, neither can you enforce any constraint. They are just separate containers.

Update: May 19, 2010 at 6:12 pm IST

Okay! So we have learnt the basics. You might need some time before you can start thinking in Cassandra DataModel’s terms.
Anyhow, let’s revise what we learnt in brief:

  1. Column is the basic data holding entity,
  2. SuperColumn contains a Map of Columns,
  3. ColumnFamily is where Cassandra stores all Columns; it loosely resembles databases’ Table.
  4. SuperColumn Family is just a ColumnFamily of SuperColumns.

Phew! It hasn’t yet got digested fully. It will take some time. 🙂

Next thing to learn about Cassandra is that it does NOT have any SQL like query features, so you can NOT sort the data when you are fetching it. Rather Cassandra sorts the data as soon as the data is put into the Cassandra clusters, and always remains sorted. Columns are sorted by their names, and the mechanism of sorting can be defined and controlled at the ColumnFamily’s definition, using “CompareWith” attribute.

Cassandra comes with following sorting options, though you can write your own sorting behavior it you need.

  1. BytesTypeSimple sort by byte value. No validation is performed.
  2. AsciiType:   Like BytesType, but validates that the input can be parsed as US-ASCII.
  3. UTF8TypeA string encoded as UTF8
  4. LongTypeA 64bit long
  5. LexicalUUIDType: A 128bit UUID, compared lexically (by byte value)
  6. TimeUUIDType: A 128bit version 1 UUID, compared by timestamp

Let’s try to understand it using some examples. Let’s say we have a raw Column set, i.e. which is not yet stored in Cassandra.

9:  {name: 9,  value: Ronald},
3:  {name: 3,  value: John},
15: {name: 15, value: Eric}

And, suppose that we have a ColumnFamily with UTF8Type sorting option.

<ColumnFamily CompareWith="UTF8Type" Name="Names"/>

Then, Cassandra will sort like,

15: {name: 15,  value: Eric},
3:  {name: 3,    value: John},
9:  {name: 9,   value: Ronald}

And with another ColumnFamily with LongType sorting option,

<ColumnFamily CompareWith="LongType" Name="Names"/>

Result will be like,

3:  {name: 3,  value: John},
9:  {name: 9,  value: Ronald},
15: {name: 15, value: Eric}

The same rules of sorting also get applied to SuperColumns. However, in this case we also need to specify a second sorting rule using the "CompareSubcolumnsWith" attribute for internal Columns’ sorting behavior.

For example consider following definition:

<ColumnFamily ColumnType="Super" CompareWith="UTF8Type"
CompareSubcolumnsWith="LongType" Name="Posts"/>

In this case, SuperColumns will be sorted by UTF8Type policy, and Columns by LongType policy.

If your need asks for custom sorting policies, you can easily write one.

  1. Create a Class extending org.apache.cassandra.db.marshal.AbstractType class.
  2. Package this class in a Java Archive and add it to the /lib folder of your Cassandra installation.
  3. Specify the fully qualified classname in the CompareSubcolumnsWith or CompareWith attribute. That’s it.

So, that’s all about Cassandra DataModels. Now, in our next step, we will write Cassandra client and see how deep the rabbit hole goes!

Written by Animesh

May 18, 2010 at 6:12 am

Posted in Technology

Tagged with , , ,