HBase ORM

Introduction

HBase ORM is a light-weight, production-grade, thread-safe and performant library that enables object-oriented access of HBase rows (Data Access Object) with minimal code and good testability.

This can also be used as an ORM for Bigtable. Scroll down till the relevant section to know how.

Usage

Let's say you've an HBase table citizens with row-key format of country_code#UID. Now, let's say this table is created with three column families main, optional and tracked, which may have columns (qualifiers) uid, name, salary etc.

This library enables to you represent your HBase table as a bean-like class, as below:

@HBTable(namepsace = "govt", name = "citizens",
  families = {
    @Family(name = "main"),
    @Family(name = "optional", versions = 3),
    @Family(name = "tracked", versions = 10)
  }
)
public class Citizen implements HBRecord<String> {

  private String countryCode;

  private Integer uid;

  @HBColumn(family = "main", column = "name")
  private String name;

  @HBColumn(family = "optional", column = "age")
  private Short age;

  @HBColumn(family = "optional", column = "income")
  private Integer annualIncome;

  @HBColumn(family = "optional", column = "registration_date")
  private LocalDateTime registrationDate;

  @HBColumn(family = "optional", column = "counter")
  private Long counter;

  @HBColumn(family = "optional", column = "custom_details")
  private Map<String, Integer> customDetails;

  @HBColumn(family = "optional", column = "dependents")
  private Dependents dependents;

  @HBColumnMultiVersion(family = "tracked", column = "phone_number")
  private NavigableMap<Long, Integer> phoneNumber;

  @HBColumn(family = "optional", column = "pincode", codecFlags = {
    @Flag(name = BestSuitCodec.SERIALIZE_AS_STRING, value = "true")
  })
  private Integer pincode;

  @Override
  public String composeRowKey() {
    return String.format("%s#%d", countryCode, uid);
  }

  @Override
  public void parseRowKey(String rowKey) {
    String[] pieces = rowKey.split("#");
    this.countryCode = pieces[0];
    this.uid = Integer.parseInt(pieces[1]);
  }

  // Constructors, getters and setters
}

That is,

• The above class Citizen represents the HBase table citizens in namespace govt, using the @HBTable annotation.
• Logics for conversion of HBase row key to member variables of Citizen objects and vice-versa are implemented using parseRowKey and composeRowKey methods respectively.
• The data type representing row key is the type parameter to HBRecord generic interface (in above case, String).
  • Note that String is both 'Comparable with itself' and Serializable.
• Names of columns and their column families are specified using @HBColumn or @HBColumnMultiVersion annotations.
• The class may contain fields of simple data types (e.g. String, Integer), generic data types (e.g. Map, List), custom class (e.g. Dependents) or even generics of custom class (e.g. List<Dependent>)
• The @HBColumnMultiVersion annotation allows you to map multiple versions of column in a NavigableMap<Long, ?>. In above example, field phoneNumber is mapped to column phone_number within the column family tracked (which is configured for multiple versions)

Alternatively, you can model your class as below:

class CitizenKey implements Serializable, Comparable<CitizenKey> {
    String countryCode;
    Integer uid;

    @Override
    public int compareTo(CitizenKey key) {
        // your custom logic involving countryCode and uid
    }
}

public class Citizen implements HBRecord<CitizenKey> {

    private CitizenKey rowKey;

    @Override
    public CitizenKey composeRowKey() {
        return rowKey;
    }

    @Override
    public void parseRowKey(CitizenKey rowKey) {
        this.rowKey = rowKey;
    }
}

See source files Citizen.java and Employee.java for detailed examples. Specifically, Employee.java demonstrates using "column inheritance" of this library, a useful feature if you have many HBase tables with common set of columns.

Serialization / Deserialization mechanism

• Serialization and deserialization are handled through 'codecs'.
• The default codec (called BestSuitCodec) included in this library has the following behavior:
  • uses HBase's native methods to serialize objects of data types Boolean, Short, Integer, Long, Float, Double, String and BigDecimal (see: Bytes)
  • uses Jackson's JSON serializer for all other data types
  • serializes null as null
• To customize serialization/deserialization behavior, you may define your own codec (by implementing the Codec interface) or you may extend the default codec.
• The optional parameter codecFlags (supported by both @HBColumn and @HBColumnMultiVersion annotations) can be used to pass custom flags to the underlying codec. (e.g. You may want your codec to serialize field Integer id in Citizen class differently from field Integer id in Employee class)
• The default codec class BestSuitCodec takes a flag BestSuitCodec.SERIALIZE_AS_STRING, whose value is "serializeAsString" (as in the above Citizen class example). When this flag is set to true on a field, the default codec serializes that field (even numerical fields) as strings.
  • Your custom codec may take other such flags as inputs to customize serialization/deserialization behavior at a class field level.

Using this library for database access (DAO)

This library provides an abstract class to define your own data access object. For example, you can create one for Citizen class in the above example as follows:

import org.apache.hadoop.hbase.client.Connection;
import com.flipkart.hbaseobjectmapper.AbstractHBDAO;
import java.io.IOException;

public class CitizenDAO extends AbstractHBDAO<String, Citizen> {
// in above, String is the 'row type' of Citizen

  public CitizenDAO(Connection connection) throws IOException {
    super(connection); // if you need to customize your codec, you may use super(connection, codec)
    // alternatively, you can construct CitizenDAO by passing instance of 'org.apache.hadoop.conf.Configuration'
  }
}

(see CitizenDAO.java)

Additionally, you can also create asynchronous DAO objects by extending ReactiveHBDAO as illustrated in the snippet below. Reactive DAOs are based on Java's CompletableFuture abstraction and require an AsyncConnection object to be supplied rather than Connection.

import com.flipkart.hbaseobjectmapper.ReactiveHBDAO;
import com.flipkart.hbaseobjectmapper.testcases.entities.Citizen;

import org.apache.hadoop.hbase.client.AsyncConnection;

public class CitizenDAO extends ReactiveHBDAO<String, Citizen> {
// in above, String is the 'row type' of Citizen

    public CitizenDAO(AsyncConnection connection) {
        super(connection); // if you need to customize your codec, you may use super(connection, codec)
        // alternatively, you can construct CitizenDAO by passing instance of 'org.apache.hadoop.conf.Configuration'
    }
}

(see reactive/CitizenDAO.java)

Once defined, you can instantiate your data access object as below:

CitizenDAO citizenDao = new CitizenDAO(connection);

Side note: As you'd know, HBase's Connection creation is a heavy-weight operation (Details: Connection). So, it is recommended that you create Connection instance once and use it for the entire life cycle of your program across all the DAO classes that you create (such as above).

Now, you can access, manipulate and persist records of citizens table as shown in below examples. Note that the signatures of the reactive DAO may differ; all operations return CompletableFutures.

Create new record:

String rowKey = citizenDao.persist(new Citizen("IND", 1, /* more params */));
// In above, output of 'persist' is a String, because Citizen class implements HBRecord<String>

Fetch a single record by its row key:

// Fetch row from "citizens" HBase table whose row key is "IND#1":
Citizen pe = citizenDao.get("IND#1");

Fetch multiple records by their row keys:

Citizen[] ape = citizenDao.get(new String[] {"IND#1", "IND#2"}); //bulk get

Fetch records by range of row keys (start row key, end row key):

List<Citizen> lpe1 = citizenDao.get("IND#1", "IND#5");
// above uses default behavior: start key inclusive, end key exclusive, 1 version

List<Citizen> lpe2 = citizenDao.get("IND#1", true, "IND#9", true, 5, 10000);
// above fetches with: start key inclusive, end key inclusive, 5 versions, caching set to 10,000 rows 

Iterate over large number of records by range of row keys:

// Read records from 'IND#000000001' (inclusive) to 'IND#100000000' (exclusive):
try (Records<Citizen> citizens = citizenDao.records("IND#000000001", "IND#100000000")) {
  for (Citizen citizen : citizens) {
    // your code
  }
}

// Read records from 'IND#000000001' (inclusive) to 'IND#100000000' (inclusive) with 
// 5 versions, caching set to 1,000 rows:
try (Records<Citizen> citizens = citizenDao.records("IND#000000001", true, "IND#100000000", true, 5, 1000)) {
  for (Citizen citizen : citizens) {
    // your code
  }
}

Note: All the .records(...) methods efficiently use iterators internally and do not load records upfront into memory. Hence, it is safe to fetch millions of records using them.

Fetch records by row key prefix:

List<Citizen> lpe3 = citizenDao.getByPrefix(citizenDao.toBytes("IND#"));

Iterate over large number of records by row key prefix:

try (Records<Citizen> citizens = citizenDao.recordsByPrefix(citizenDao.toBytes("IND#"))) {
  for (Citizen citizen : citizens) {
    // do something
  }
}

Fetch records by HBase's native Scan object: (for very advanced access patterns)

Scan scan = new Scan().setAttribute(...)
  .setReadType(...)
  .setACL(...)
  .withStartRow(...)
  .withStopRow(...)
  .readAllVersions(...);
try (Records<Citizen> citizens = citizenDao.records(scan)) {
  for (Citizen citizen : citizens) {
    // do something
  }
}

Fetch specific field(s) for given row key(s):

// for row keys in range ["IND#1", "IND#5"), fetch 3 versions of field 'phoneNumber':
NavigableMap<String, NavigableMap<Long, Object>> phoneNumberHistory 
  = citizenDao.fetchFieldValues("IND#1", "IND#5", "phoneNumber", 3);
// bulk variants of above range method are also available

Read data from HBase using HBase's native Get:

Get get1 = citizenDao.getGet("IND#2");
// above returns object of HBase's Get corresponding to row key "IND#2", to enable advanced read patterns
Counter counter1 = counterDAO.getOnGet(get1);

Get get2 = citizenDao.getGet("IND#2").setTimeRange(1, 5).setMaxVersions(2); // Advanced HBase row fetch
Counter counter2 = counterDAO.getOnGet(get2);

Manipulate and persist an object back to HBase:

// change a field:
pe.setPincode(560034);

// Save the record back to HBase:
citizenDao.persist(pe); 

Delete records in various ways:

// Delete a row by its object reference:
citizenDao.delete(pe);

// Delete multiple rows by list of object references:
citizenDao.delete(Arrays.asList(pe1, pe2)); 

// Delete a row by its row key:
citizenDao.delete("IND#2"); 

 // Delete a bunch of rows by their row keys:
citizenDao.delete(new String[] {"IND#3", "IND#4"});

Increment a column in HBase:

// Increment value of counter by 3:
citizenDao.increment("IND#2", "counter", 3L); 

Append to a column:

citizenDao.append("IND#2", "name", " Kalam");
// there are 'bulk methods' available

Other operations:

Table citizenTable = citizenDao.getHBaseTable()
// in case you want to directly class HBase's native methods

(see TestsAbstractHBDAO.java for more detailed examples)

Please note: Since we're dealing with HBase (and not a classical RDBMS), fitting a Hibernate-like ORM may not make sense. So, this library does not intend to evolve as a full-fledged ORM. However, if that's your intent, I suggest you use Apache Phoenix.

Using this library for DDL operations

The provided HBAdmin class helps you programatically create/delete tables.

You may instantiate the class using Connection or AsyncConnection object:

import org.apache.hadoop.hbase.client.Connection;
import com.flipkart.hbaseobjectmapper.HBAdmin;

// some code

HBAdmin hbAdmin = HBAdmin.create(connection);

Once instantiated, you may do the following DDL operations:

hbAdmin.createTable(Citizen.class); 
// Above statement creates table with name and column families specification as per
// the @HBTable annotation on the Citizen class

hbAdmin.tableExists(Citizen.class); // returns true/false

hbAdmin.disableTable(Citizen.class);

hbAdmin.deleteTable(Citizen.class);

Note that DDL operations on HBase are typically heavy and time-consuming.

Using this library to handle HBase data types

The HBObjectMapper class in this library provides the useful methods such as below:

Result writeValueAsResult(T record)

T readValue(Result result, Class<T> clazz)

where T is your bean-like class that extends this library's HBRecord interface (e.g. Citizen class above).

Using these, you can convert your object to HBase's Result and vice versa.

Using this library in MapReduce jobs

Read article.

Advantages

• Your application code will be clean and minimal.
• Your code need not worry about HBase methods or serialization/deserialization at all, thereby helping you maintain clear separation of concerns.
• Classes are thread-safe. You just have to instantiate your DAO classes once at the start of your application and use them anywhere throughout the life-cycle of your application!
• Light weight: This library depends on just hbase-client and few other small libraries. It has very low overhead and hence is very fast.
• Customizability/Extensibility: Want to use HBase's native methods directly in some cases? You can do that. Want to customize serialization/deserialization for a given type or for a specific given class field? You can do that too. This library is very flexible.

Limitations

Being an object mapper, this library works for predefined columns only. For example, this library doesn't provide ways to fetch:

• columns matching a pattern or a regular expression
• unmapped columns of a column family

Adding to your build

If you are using Maven, add below entry within the dependencies section of your pom.xml:

<dependency>
  <groupId>com.flipkart</groupId>
  <artifactId>hbase-object-mapper</artifactId>
  <version>1.19</version>
</dependency>

See artifact details: com.flipkart:hbase-object-mapper on Maven Central.

If you're using Gradle or Ivy or SBT, see how to include this library in your build: com.flipkart:hbase-object-mapper:1.19.

How to build?

To build this project, follow below simple steps:

1. Do a git clone of this repository
2. Checkout latest stable version git checkout v1.19
3. Execute mvn clean install from shell

Please note:

• Currently, systems that use this library are running on HBase 2.0. However, if you are using a different version, just change the version in pom.xml to the desired one and build the project.
• Test cases are very comprehensive. So, mvn build times can sometimes be longer, depending on your machine configuration.
• By default, test cases spin an in-memory HBase test cluster to run data access related test cases (near-realworld scenario).
  • If test cases are failing with time out errors, you may increase the timeout by setting environment variable INMEMORY_CLUSTER_START_TIMEOUT (seconds). For example, on Linux you may run the command export INMEMORY_CLUSTER_START_TIMEOUT=8 on terminal, before running the aforementioned mvn command.
• You may direct test cases to use an actual HBase cluster (instead of default in-memory one) by setting USE_REAL_HBASE environmental variable to true.
  • If you're using this option, ensure you've correct settings in your hbase-site.xml.
• Test cases check for a lot of 'boundary conditions'. So, you'll see a lot of exceptions in logs. They are not failures.

Releases

The change log can be found in the releases section.

Feature requests and bug reporting

If you intend to request a feature or report a bug, you may use Github Issues for hbase-orm.

Bigtable ORM

Google's Cloud Bigtable provides first-class support for accessing Bigtable using HBase client.

This library can be used as a Bigtable ORM in 3 simple steps:

1. Add following to your dependencies:
   • bigtable-hbase-2.x or bigtable-hbase-2.x-shaded, depending on your requirement
   • This library
2. Instantiate HBase client's Connection class as below:

   import com.google.cloud.bigtable.hbase.BigtableConfiguration;
   import org.apache.hadoop.hbase.client.Connection;
   // some code
   Connection connection = BigtableConfiguration.connect(projectId, instanceId);
   // some code
   

3. Use Connection instance as mentioned earlier in this README, to create your DAO class

That's it! Now you're all set to access Bigtable.

License

Copyright 2023 Flipkart Internet Pvt Ltd.

Licensed under the Apache License, version 2.0 (the "License"). You may not use this product or its source code except in compliance with the License.