The above is a list of HBase tutorial topics that you can expand on according to your teaching style and the needs of your students.<\/p>\n\n\n\n Please explain in detail what HBase is and what its basic architecture looks like<\/p>\n\n\n\n Of course, I’ll be happy to explain to you what HBase is and what its basic architecture looks like.<\/p>\n\n\n\n HBase is an open source distributed columnar database built on top of Hadoop. HBase is designed to handle very large datasets and allows horizontal scaling on large clusters. HBase is designed to handle large amounts of sparse data, and it can be used as a distributed, column-oriented NoSQL database. It is widely used for real-time random read\/write access to Hadoop data.<\/p>\n\n\n\n The basic architecture of HBase is as follows:<\/p>\n\n\n\n In summary, HBase is a highly scalable distributed database that uses a column-based storage model to store data and Hadoop’s distributed file system as the storage backend. Its basic architecture is a cluster of multiple RegionServers and uses Zookeeper as a distributed coordination service.<\/p>\n\n\n\n Please explain the installation and configuration of HBase in detail<\/p>\n\n\n\n Of course, I will be happy to explain the installation and configuration of HBase for you.<\/p>\n\n\n\n The installation and configuration of HBase can be divided into the following steps:<\/p>\n\n\n\n In summary, installing and configuring HBase requires preparing the environment, downloading and unpacking HBase, configuring HBase, starting HBase, and verifying that HBase is working properly. Some important options in the configuration file include hbase.rootdir, hbase.zookeeper.quorum, hbase.cluster.distributed, hbase.master.port, and hbase.regionserver.port.<\/p>\n\n\n\n Please explain in detail the basic operations and management of HBase, including table creation, insertion, update, deletion, and querying data.<\/p>\n\n\n\n Of course, I will be happy to explain the basic operations and management of HBase, including table creation, insertion, update, deletion, and querying data.<\/p>\n\n\n\n a. Open the HBase Shell. b. Use the create command to create a table. The syntax is: create ‘tablename’, ‘columnfamily1’, ‘columnfamily2’, … Use the debug command to check if the structure of the table is correct.<\/p>\n\n\n\n For example, to create a table named students with two column families named info and score, you can run the following command in HBase Shell:<\/p>\n\n\n\n a. Open the HBase Shell. b. Insert data using the put command. The syntax is: put ‘tablename’, ‘rowkey’, ‘columnfamily:column’, ‘value’<\/p>\n\n\n\n For example, to insert the name and age of a student with student ID 101 into the students table, you can run the following command in HBase Shell:<\/p>\n\n\n\n a. Open the HBase Shell. b. Use the put command to update the data. The syntax is the same as inserting data, except that the row key and column family\/column must match the row and column being updated.<\/p>\n\n\n\n For example, to update the age of a student with a student ID of 101 to 22 years old, you can run the following command in HBase Shell:<\/p>\n\n\n\n a. Open the HBase Shell. b. Use the delete command to delete the data. The syntax is: delete ‘tablename’, ‘rowkey’, ‘columnfamily:column’<\/p>\n\n\n\n For example, to remove the age of a student with a student ID of 101, you can run the following command in HBase Shell:<\/p>\n\n\n\n a. Scan command: Used to scan the entire table and return the result for all rows. b. get command: Used to retrieve the values of the specified row and column. c. count command: Used to count the number of rows in a table.<\/p>\n\n\n\n For example, to scan the information of all students in the students table, you can run the following command in HBase Shell:<\/p>\n\n\n\n In summary, the basic operations and management of HBase include table creation, insertion, update, deletion, and querying data. These operations can be easily performed using commands in the HBase Shell. When you create a table, you specify column families. When inserting data, you specify the row key, column family, and<\/p>\n\n\n\n Please continue<\/p>\n\n\n\n Column. When you update and delete data, you specify the appropriate row keys, column families, and columns. For query operations, you can use commands such as scan, get, and count to retrieve data.<\/p>\n\n\n\n Please give sample code for creating, inserting, updating, deleting, and querying data for tables where Java manipulates HBase<\/p>\n\n\n\n Of course, I’d be happy to provide you with sample code for creating, inserting, updating, deleting, and querying data for tables where Java manipulates HBase. Here’s a simple example:<\/p>\n\n\n\n \u8bf7\u7ee7\u7eed<\/p>\n\n\n\n The above example code uses the HBase API to manipulate HBase tables. You need to configure the HBase connection in the code, and then use the Table interface to perform table operations. The operations in each example are done by creating corresponding objects such as Put, Get, and Delete.<\/p>\n\n\n\n Please give sample code for Scala to manipulate the creation, insert, update, delete, and query data of HBase tables<\/p>\n\n\n\n Of course, I’d be happy to provide you with sample code for creating, inserting, updating, deleting, and querying data for tables where Scala manipulates HBase. Here’s a simple example:<\/p>\n\n\n\n \u8bf7\u7ee7\u7eed<\/p>\n\n\n\n main(args: Array[String]): Unit = {<\/p>\n\n\n\n } }<\/p>\n\n\n\n Please continue<\/p>\n\n\n\n Note that in the Scala sample code, we used HBase’s Scala API to manipulate HBase tables. These APIs are very similar to the Java API, but have a more scala-based syntax. For example, in Scala, we can use a for loop to iterate through multiple KeyValues in a Result object:<\/p>\n\n\n\n Note that all API calls used in the sample code can be found in HBase’s Java API. So, if you are familiar with Java, you can easily convert Java sample code to Scala code and vice versa.<\/p>\n\n\n\n Please give sample code for creating, inserting, updating, deleting, and querying data for tables in which PySpark manipulates HBase<\/p>\n\n\n\n Of course, I’d be happy to provide you with sample code for creating, inserting, updating, deleting, and querying data for tables where PySpark manipulates HBase. Here’s a simple example:<\/p>\n\n\n\n Please continue<\/p>\n\n\n\n .stop()<\/p>\n\n\n\n Note that in the above example code, we used HBase’s Spark API to manipulate HBase tables. These APIs are very similar to HBase’s Java and Scala APIs, but have a more Sparkized syntax. For example, in PySpark, we can use the DataFrame API to insert, update, delete, and query data. You need to configure the HBase connection in your code and use the data source to read and write HBase tables. Please explain the HBase data model in detail, such as row keys, column families, column modifiers, etc<\/p>\n\n\n\n HBase’s data model is based on Bigtable’s data model and adopts distributed storage. HBase’s data model consists of rows, column families, column modifiers, and timestamps, where the row key is the primary key of the table. Below we will take a closer look at HBase’s data model.<\/p>\n\n\n\n In HBase, a row key is the primary key of a table that uniquely identifies each row of data in a table. The row key is a byte array and can be of any length. Row keys are sorted lexicographically, so data can be retrieved quickly based on the value of the row key. In HBase, row keys are required, and row keys must be unique for each row.<\/p>\n\n\n\n In HBase, a column family is a collection of columns. Column families are defined when the table is created and cannot be changed. All columns must belong to a column family. Column families are logically similar to columns in a relational database, but are physically stored separately.<\/p>\n\n\n\n In HBase, a column modifier refers to a column in a column family. The column modifier is a byte array that can be of any length. Column modifiers are used to further identify columns in a column family, so that each column has a unique identifier.<\/p>\n\n\n\n In HBase, each cell has a timestamp that identifies the version of the data. The timestamp can be an integer of any length or a long integer. When inserting or updating data into a table, you can specify a timestamp or use the system default timestamp.<\/p>\n\n\n\n In summary, HBase’s data model consists of rows, column families, column modifiers, and timestamps. The row key is the primary key of the table and uniquely identifies each row of data in the table. A column family is a collection of columns, and all columns must belong to a column family. Column modifiers are used to further identify columns in a column family, so that each column has a unique identifier. Timestamps are used to identify the version of data and can be integers of any length or long integers. HBase’s data model is a very flexible and scalable data model that can handle massive amounts of data and supports fast data retrieval.<\/p>\n\n\n\n Please explain HBase’s filters in detail, such as row key filters, column filters, and single-column value filters<\/p>\n\n\n\n HBase provides a variety of filters to query data in a table, including row key filters, column filters, and single-column value filters. These filters are filtered based on rows, column families, column modifiers, and timestamps in HBase’s data model. Below we will introduce the common filters of HBase.<\/p>\n\n\n\n Rowkey filters are used to filter data by rowkey key. HBase provides a variety of row key filters, such as comparator filters, regular expression filters, and so on.<\/p>\n\n\n\n Comparator filters are used to compare the values of row keys. It can be used to filter rows that are equal to, not equal to, less than, less than or equal to, greater than, greater than or equal to a certain value.<\/p>\n\n\n\n Regular expression filters are used to match the value of row keys by regular expression.<\/p>\n\n\n\n Column filters are used to filter data by column family and column modifier. HBase provides a variety of column filters, such as prefix filters, multiple column filters, and so on.<\/p>\n\n\n\n Prefix filters are used to match the prefixes of column modifiers.<\/p>\n\n\n\n Multiple column filters are used to match multiple columns.<\/p>\n\n\n\n Single-column value filters are used to filter data by the value of a column. It can be used to filter rows that are equal to, not equal to, less than, less than or equal to, greater than, greater than or equal to a certain value.<\/p>\n\n\n\n HBase also supports combined filters, which can be combined to achieve more complex filtering functions. The combination filter supports logical operators AND, OR, NOT, and can be freely combined as needed.<\/p>\n\n\n\n HBase’s filters are an important tool for HBase to query data. Using filters, data in HBase tables can be queried quickly and flexibly while avoiding unnecessary data transfer and processing.<\/p>\n\n\n\n Please explain in detail the integration of HBase with Hadoop<\/p>\n\n\n\n HBase is built on top of Hadoop, which leverages the capabilities of Hadoop’s distributed file system HDFS and the distributed computing framework MapReduce. The integration of Hadoop and HBase is very tight, enabling the storage and efficient processing of massive amounts of data, while providing good scalability and reliability.<\/p>\n\n\n\n HDFS is Hadoop’s distributed file system that provides high-throughput data access and fault tolerance to store petabytes of data. HBase uses HDFS to store data, and each HBase table is divided into multiple regions, each of which is stored in a single file on HDFS. Therefore, HBase needs to use HDFS’s API to access and manipulate data on HDFS.<\/p>\n\n\n\n MapReduce is Hadoop’s distributed computing framework that supports distributed computing and data processing. HBase can use MapReduce for data analysis and processing. MapReduce can read data from HBase tables and use multiple nodes in a Hadoop cluster for data processing. HBase also provides the HBase InputFormat and HBase OutputFormat, which can directly use HBase tables as inputs and outputs to MapReduce.<\/p>\n\n\n\n ZooKeeper is a distributed coordination service commonly used in Hadoop and HBase, which can coordinate tasks and data between multiple nodes, ensuring system reliability and consistency. HBase uses ZooKeeper to manage regions in the cluster, and ZooKeeper also provides a reliable leader election mechanism to ensure high availability and reliability of RegionServer in HBase clusters.<\/p>\n\n\n\n In short, HBase and Hadoop are very tightly integrated, which can realize the storage and efficient processing of massive data, and provide good scalability and reliability. Hadoop provides HDFS and MapReduce capabilities, and HBase builds on top of that, providing column-based distributed storage and read and write capabilities while leveraging ZooKeeper for orchestration and management.<\/p>\n\n\n\n Please explain in detail the use of MapReduce, an advanced feature of HBase<\/p>\n\n\n\n HBase can be integrated with MapReduce, Hadoop’s distributed computing framework, which can be used for large-scale data analysis and processing. The main steps to use MapReduce in HBase include:<\/p>\n\n\n\n To use MapReduce in HBase, you need to define the input and output formats of the MapReduce job. HBase provides HBaseInputFormat and HBaseOutputFormat, which can directly use HBase tables as input and output for MapReduce jobs.<\/p>\n\n\n\n Mapper is the first stage of a MapReduce job that converts input data into a set of key-value pairs. Using MapReduce in HBase, Mapper needs to read data from the input HBase table and convert it into key-value pairs.<\/p>\n\n\n\n Reduce is the second stage of the MapReduce job, which is used to merge and summarize the key-value pairs output by Mapper. Using MapReduce in HBase, Reducer aggregates and counts the key-value pairs output by Mapper.<\/p>\n\n\n\n To use MapReduce in HBase, you need to configure MapReduce job parameters, such as input and output paths, Mapper and Reduce classes, input and output formats, etc.<\/p>\n\n\n\n After you finish writing and configuring the MapReduce job, you can submit the MapReduce job and wait for the result. With MapReduce in HBase, MapReduce jobs are executed on multiple nodes in a Hadoop cluster to enable parallel processing of data.<\/p>\n\n\n\n HBase’s MapReduce function can be used for the analysis and processing of massive amounts of data. For example, you can use MapReduce to count statistics such as the amount of data, average, maximum, and minimum value of a column family or column in an HBase table. MapReduce can also be used in scenarios such as data cleaning, data conversion, and data association, and can be flexibly applied according to specific needs.<\/p>\n\n\n\n In short, HBase’s MapReduce feature provides an efficient and reliable distributed data analysis and processing method, which can be tightly integrated with Hadoop’s distributed computing framework to achieve massive data processing and analysis.<\/p>\n\n\n\n Please explain in detail the transaction processing of HBase, etc<\/p>\n\n\n\n HBase’s transaction processing capabilities are very important in distributed systems to ensure data consistency and reliability. In HBase, transaction processing mainly includes two aspects, one is the support of ACID transactions, and the other is multi-version concurrency control.<\/p>\n\n\n\n HBase supports column family-based ACID transactions, which ensure that when operating on multiple rows in a transaction, the transaction will be committed only after all operations are successful, otherwise the transaction will be rolled back. In HBase, a transaction can include multiple operations, including inserting, updating, and deleting data. HBase uses MVCC (Multi-Version Concurrency Control) to support ACID transactions, that is, storing raw data in each version to support concurrent transactions and snapshot reads.<\/p>\n\n\n\n HBase uses multi-version concurrency control (MVCC) to support concurrent transactions and snapshot reads. MVCC manages concurrent transactions by assigning a unique timestamp to each version. When new data is written, a new timestamp is assigned to the new data and the new data is written to disk. When reading data, the appropriate version is returned to the user based on the timestamp of the read operation. This approach ensures that data access between multiple transactions does not affect each other, and snapshot reading can be realized.<\/p>\n\n\n\n In short, HBase’s transaction processing capabilities can ensure data consistency and reliability. HBase supports column family-based ACID transactions, which ensure that when operating on multiple rows in a transaction, the transaction will be committed only after all operations are successful, otherwise the transaction will be rolled back. At the same time, HBase also uses MVCC to support concurrent transaction processing and snapshot reading, which can ensure that data access between multiple transactions will not affect each other, and snapshot reading can be realized.<\/p>\n\n\n\n \u8bf7\u8be6\u7ec6\u89e3\u91caHBase\u7684\u6027\u80fd\u4f18\u5316\uff0c\u5982\u8868\u8bbe\u8ba1\u4f18\u5316\u3001\u7f13\u5b58\u4f18\u5316\u3001I\/O\u4f18\u5316\u7b49\u3002<\/p>\n\n\n\n HBase\u7684\u6027\u80fd\u4f18\u5316\u662f\u975e\u5e38\u91cd\u8981\u7684\uff0c\u53ef\u4ee5\u63d0\u9ad8\u7cfb\u7edf\u7684\u541e\u5410\u91cf\u548c\u54cd\u5e94\u65f6\u95f4\uff0c\u540c\u65f6\u964d\u4f4e\u8d44\u6e90\u6d88\u8017\u548c\u6210\u672c\u3002HBase\u7684\u6027\u80fd\u4f18\u5316\u4e3b\u8981\u5305\u62ec\u8868\u8bbe\u8ba1\u4f18\u5316\u3001\u7f13\u5b58\u4f18\u5316\u3001I\/O\u4f18\u5316\u7b49\u3002<\/p>\n\n\n\n Table design is one of the key factors affecting HBase’s performance. When designing a table structure, you should consider the following aspects:<\/p>\n\n\n\n HBase’s caching mechanism is important to improve the responsiveness and performance of the system. HBase caches are divided into block cache and row cache:<\/p>\n\n\n\n HBase’s I\/O performance is also one of the important factors affecting system performance. HBase’s I\/O optimization mainly includes the following aspects:<\/p>\n\n\n\n In short, HBase’s performance optimization needs to be derived from table design, cache optimization, I\/O optimization, and so on<\/p>\n\n\n\n Please continue<\/p>\n\n\n\n Start with multiple aspects, adjust and optimize according to the specific situation. Here are some common HBase performance optimization tips:<\/p>\n\n\n\n In short, the performance optimization of HBase needs to be adjusted and optimized according to the specific situation, and can start from multiple aspects such as table design, cache optimization, and I\/O optimization to improve the performance and reliability of the system.<\/p>\n\n\n\n Please explain in detail the monitoring and management of HBase, including HBase’s logs, monitoring metrics, and management tools.<\/p>\n\n\n\n HBase monitoring and management is one of the important means to ensure system reliability and stability. In HBase, you can monitor and manage the system through logs, monitoring metrics, and management tools.<\/p>\n\n\n\n HBase logs include two types: RegionServer logs and Master logs. RegionServer logs mainly record the running status and abnormal conditions of RegionServer, including data reading and writing, RegionServer load, error information, etc. The Master log mainly records the running status and abnormal conditions of the Master, including the startup and shutdown of RegionServer, load balancing, etc.<\/p>\n\n\n\n HBase\u7684\u65e5\u5fd7\u53ef\u4ee5\u901a\u8fc7\u65e5\u5fd7\u5206\u6790\u5de5\u5177\uff08\u5982Log4j\u3001Logstash\uff09\u8fdb\u884c\u76d1\u63a7\u548c\u5206\u6790\uff0c\u4ee5\u53ca\u53ca\u65f6\u53d1\u73b0\u548c\u5904\u7406\u7cfb\u7edf\u7684\u5f02\u5e38\u60c5\u51b5\u3002<\/p>\n\n\n\n HBase provides a number of monitoring metrics that can be used to monitor the status and performance of your system, including:<\/p>\n\n\n\n HBase’s monitoring metrics can be monitored and analyzed in real time through JMX, Ganglia and other monitoring tools to detect and deal with system anomalies in a timely manner.<\/p>\n\n\n\n HBase provides several management tools that can be used to manage and maintain the system, including:<\/p>\n\n\n\n In summary, HBase’s monitoring and management tools help administrators monitor and manage HBase clusters, including logs, monitoring metrics, and management tools. These tools can help administrators find and deal with system anomalies in a timely manner, improving system reliability and stability.<\/p>\n","protected":false},"excerpt":{"rendered":" I want to do a video … <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_mi_skip_tracking":false,"footnotes":""},"categories":[23],"tags":[],"class_list":["post-1205","post","type-post","status-publish","format-standard","hentry","category-material"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=\/wp\/v2\/posts\/1205","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1205"}],"version-history":[{"count":1,"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=\/wp\/v2\/posts\/1205\/revisions"}],"predecessor-version":[{"id":1206,"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=\/wp\/v2\/posts\/1205\/revisions\/1206"}],"wp:attachment":[{"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1205"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1205"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/edu.ujhb.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/figure>\n\n\n\n
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create 'students', 'info', 'score'\n<\/code><\/pre>\n\n\n\n\n
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put 'students', '101', 'info:name', 'John'\nput 'students', '101', 'info:age', '20'\n<\/code><\/pre>\n\n\n\n\n
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put 'students', '101', 'info:age', '22'\n<\/code><\/pre>\n\n\n\n\n
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delete 'students', '101', 'info:age'\n<\/code><\/pre>\n\n\n\n\n
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scan 'students'\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
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import org.apache.hadoop.conf.Configuration;\nimport org.apache.hadoop.hbase.HBaseConfiguration;\nimport org.apache.hadoop.hbase.TableName;\nimport org.apache.hadoop.hbase.client.Admin;\nimport org.apache.hadoop.hbase.client.Connection;\nimport org.apache.hadoop.hbase.client.ConnectionFactory;\nimport org.apache.hadoop.hbase.client.TableDescriptor;\nimport org.apache.hadoop.hbase.client.TableDescriptorBuilder;\nimport org.apache.hadoop.hbase.io.compress.CompressionAlgorithm;\nimport org.apache.hadoop.hbase.regionserver.BloomType;\nimport org.apache.hadoop.hbase.util.Bytes;\n\npublic class CreateTableExample {\n public static void main(String[] args) throws Exception {\n\n Configuration conf = HBaseConfiguration.create();\n Connection connection = ConnectionFactory.createConnection(conf);\n Admin admin = connection.getAdmin();\n\n \/\/ \u521b\u5efa\u8868\u683c\u63cf\u8ff0\u7b26\n TableDescriptor tableDescriptor = TableDescriptorBuilder\n .newBuilder(TableName.valueOf(\"students\"))\n .setColumnFamily(ColumnFamilyDescriptorBuilder.of(\"info\"))\n .setColumnFamily(ColumnFamilyDescriptorBuilder.of(\"score\"))\n .setCompactionEnabled(true)\n .setCompressionType(CompressionAlgorithm.GZ)\n .setBloomFilterType(BloomType.ROWCOL)\n .build();\n\n \/\/ \u521b\u5efa\u8868\u683c\n admin.createTable(tableDescriptor);\n\n System.out.println(\"Table created\");\n admin.close();\n connection.close();\n }\n}\n<\/code><\/pre>\n\n\n\n\n
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import org.apache.hadoop.conf.Configuration;\nimport org.apache.hadoop.hbase.HBaseConfiguration;\nimport org.apache.hadoop.hbase.TableName;\nimport org.apache.hadoop.hbase.client.Connection;\nimport org.apache.hadoop.hbase.client.ConnectionFactory;\nimport org.apache.hadoop.hbase.client.Put;\nimport org.apache.hadoop.hbase.util.Bytes;\n\npublic class InsertDataExample {\n public static void main(String[] args) throws Exception {\n\n Configuration conf = HBaseConfiguration.create();\n Connection connection = ConnectionFactory.createConnection(conf);\n\n Table table = connection.getTable(TableName.valueOf(\"students\"));\n\n \/\/ \u63d2\u5165\u6570\u636e\n Put put = new Put(Bytes.toBytes(\"101\"));\n put.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"name\"), Bytes.toBytes(\"John\"));\n put.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"), Bytes.toBytes(\"20\"));\n table.put(put);\n\n System.out.println(\"Data inserted\");\n table.close();\n connection.close();\n }\n}\n<\/code><\/pre>\n\n\n\n\n
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import org.apache.hadoop.conf.Configuration;\nimport org.apache.hadoop.hbase.HBaseConfiguration;\nimport org.apache.hadoop.hbase.TableName;\nimport org.apache.hadoop.hbase.client.Connection;\nimport org.apache.hadoop.hbase.client.ConnectionFactory;\nimport org.apache.hadoop.hbase.client.Put;\nimport org.apache.hadoop.hbase.client.Table;\nimport org.apache.hadoop.hbase.util.Bytes;\n\npublic class UpdateDataExample {\n public static void main(String[] args) throws Exception {\n\n Configuration conf = HBaseConfiguration.create();\n Connection connection = ConnectionFactory.createConnection(conf);\n\n Table table = connection.getTable(TableName.valueOf(\"students\"));\n\n \/\/ \u66f4\u65b0\u6570\u636e\n Put put = new Put(Bytes.toBytes(\"101\"));\n put.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"), Bytes.toBytes(\"22\"));\n table.put(put);\n\n System.out.println(\"Data updated\");\n table.close();\n connection.close();\n }\n}\n<\/code><\/pre>\n\n\n\n\n
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import org.apache.hadoop.conf.Configuration;\nimport org.apache.hadoop.hbase.HBaseConfiguration;\nimport org.apache.hadoop.hbase.TableName;\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
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import org.apache.hadoop.hbase.client.Connection;\nimport org.apache.hadoop.hbase.client.ConnectionFactory;\nimport org.apache.hadoop.hbase.client.Delete;\nimport org.apache.hadoop.hbase.client.Table;\nimport org.apache.hadoop.hbase.util.Bytes;\n\npublic class DeleteDataExample {\n public static void main(String[] args) throws Exception {\n\n Configuration conf = HBaseConfiguration.create();\n Connection connection = ConnectionFactory.createConnection(conf);\n\n Table table = connection.getTable(TableName.valueOf(\"students\"));\n\n \/\/ \u5220\u9664\u6570\u636e\n Delete delete = new Delete(Bytes.toBytes(\"101\"));\n delete.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"));\n table.delete(delete);\n\n System.out.println(\"Data deleted\");\n table.close();\n connection.close();\n }\n}\n<\/code><\/pre>\n\n\n\n\n
javaCopy code
import org.apache.hadoop.conf.Configuration;\nimport org.apache.hadoop.hbase.HBaseConfiguration;\nimport org.apache.hadoop.hbase.TableName;\nimport org.apache.hadoop.hbase.client.Connection;\nimport org.apache.hadoop.hbase.client.ConnectionFactory;\nimport org.apache.hadoop.hbase.client.Get;\nimport org.apache.hadoop.hbase.client.Result;\nimport org.apache.hadoop.hbase.client.Table;\nimport org.apache.hadoop.hbase.util.Bytes;\n\npublic class QueryDataExample {\n public static void main(String[] args) throws Exception {\n\n Configuration conf = HBaseConfiguration.create();\n Connection connection = ConnectionFactory.createConnection(conf);\n\n Table table = connection.getTable(TableName.valueOf(\"students\"));\n\n \/\/ \u83b7\u53d6\u6570\u636e\n Get get = new Get(Bytes.toBytes(\"101\"));\n Result result = table.get(get);\n byte[] name = result.getValue(Bytes.toBytes(\"info\"), Bytes.toBytes(\"name\"));\n byte[] age = result.getValue(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"));\n\n System.out.println(\"Name: \" + Bytes.toString(name));\n System.out.println(\"Age: \" + Bytes.toString(age));\n table.close();\n connection.close();\n }\n}\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
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scalaCopy code
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}\nimport org.apache.hadoop.hbase.client.{Admin, ColumnFamilyDescriptorBuilder, Connection, ConnectionFactory, TableDescriptor, TableDescriptorBuilder}\n\nobject CreateTableExample {\n def main(args: Array[String]): Unit = {\n\n val conf = HBaseConfiguration.create()\n val connection = ConnectionFactory.createConnection(conf)\n val admin: Admin = connection.getAdmin\n\n \/\/ \u521b\u5efa\u8868\u683c\u63cf\u8ff0\u7b26\n val tableDescriptor: TableDescriptor = TableDescriptorBuilder\n .newBuilder(TableName.valueOf(\"students\"))\n .setColumnFamily(ColumnFamilyDescriptorBuilder.of(\"info\"))\n .setColumnFamily(ColumnFamilyDescriptorBuilder.of(\"score\"))\n .build()\n\n \/\/ \u521b\u5efa\u8868\u683c\n admin.createTable(tableDescriptor)\n\n println(\"Table created\")\n admin.close()\n connection.close()\n }\n}\n<\/code><\/pre>\n\n\n\n\n
scalaCopy code
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}\nimport org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Put}\nimport org.apache.hadoop.hbase.util.Bytes\n\nobject InsertDataExample {\n def main(args: Array[String]): Unit = {\n\n val conf = HBaseConfiguration.create()\n val connection = ConnectionFactory.createConnection(conf)\n\n val table = connection.getTable(TableName.valueOf(\"students\"))\n\n \/\/ \u63d2\u5165\u6570\u636e\n val put = new Put(Bytes.toBytes(\"101\"))\n put.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"name\"), Bytes.toBytes(\"John\"))\n put.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"), Bytes.toBytes(\"20\"))\n table.put(put)\n\n println(\"Data inserted\")\n table.close()\n connection.close()\n }\n}\n<\/code><\/pre>\n\n\n\n\n
scalaCopy code
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}\nimport org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Put}\nimport org.apache.hadoop.hbase.util.Bytes\n\nobject UpdateDataExample {\n def main(args: Array[String]): Unit = {\n\n val conf = HBaseConfiguration.create()\n val connection = ConnectionFactory.createConnection(conf)\n\n val table = connection.getTable(TableName.valueOf(\"students\"))\n\n \/\/ \u66f4\u65b0\u6570\u636e\n val put = new Put(Bytes.toBytes(\"101\"))\n put.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"), Bytes.toBytes(\"22\"))\n table.put(put)\n\n println(\"Data updated\")\n table.close()\n connection.close()\n }\n}\n<\/code><\/pre>\n\n\n\n\n
scalaCopy code
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}\nimport org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Delete}\nimport org.apache.hadoop.hbase.util.Bytes\n\nobject DeleteDataExample {\n def main(args: Array[String]): Unit = {\n\n val conf = HBaseConfiguration.create()\n val connection = ConnectionFactory.createConnection(conf)\n\n val table = connection.getTable(TableName.valueOf(\"students\"))\n\n \/\/ \u5220\u9664\u6570\u636e\n val delete = new Delete(Bytes.toBytes(\"101\"))\n delete.addColumn(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"))\n table.delete(delete)\n\n println(\"Data deleted\")\n table.close()\n connection.close()\n }\n}\n<\/code><\/pre>\n\n\n\n\n
scalaCopy code
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}\nimport org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Get}\nimport org.apache.hadoop.hbase.util.Bytes\n\nobject QueryDataExample {\n def\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
lessCopy code
val conf = HBaseConfiguration.create()\nval connection = ConnectionFactory.createConnection(conf)\n\nval table = connection.getTable(TableName.valueOf(\"students\"))\n\n\/\/ \u83b7\u53d6\u6570\u636e\nval get = new Get(Bytes.toBytes(\"101\"))\nval result = table.get(get)\nval name = result.getValue(Bytes.toBytes(\"info\"), Bytes.toBytes(\"name\"))\nval age = result.getValue(Bytes.toBytes(\"info\"), Bytes.toBytes(\"age\"))\n\nprintln(\"Name: \" + Bytes.toString(name))\nprintln(\"Age: \" + Bytes.toString(age))\ntable.close()\nconnection.close()\n<\/code><\/pre>\n\n\n\nmathematicaCopy code
\n\u4ee5\u4e0a\u793a\u4f8b\u4ee3\u7801\u4e2d\u4f7f\u7528\u4e86HBase API\u6765\u64cd\u4f5cHBase\u8868\u3002\u60a8\u9700\u8981\u5728\u4ee3\u7801\u4e2d\u914d\u7f6eHBase\u8fde\u63a5\uff0c\u7136\u540e\u4f7f\u7528Table\u63a5\u53e3\u6765\u6267\u884c\u8868\u7684\u64cd\u4f5c\u3002\u6bcf\u4e2a\u793a\u4f8b\u4e2d\u7684\u64cd\u4f5c\u90fd\u662f\u901a\u8fc7\u521b\u5efa\u76f8\u5e94\u7684\u5bf9\u8c61\uff08\u5982Put\u3001Get\u548cDelete\uff09\u6765\u5b8c\u6210\u7684\u3002\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
scalaCopy code
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}\nimport org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Get}\nimport org.apache.hadoop.hbase.util.Bytes\n\nobject QueryDataExample {\n def main(args: Array[String]): Unit = {\n\n val conf = HBaseConfiguration.create()\n val connection = ConnectionFactory.createConnection(conf)\n\n val table = connection.getTable(TableName.valueOf(\"students\"))\n\n \/\/ \u83b7\u53d6\u6570\u636e\n val get = new Get(Bytes.toBytes(\"101\"))\n val result = table.get(get)\n\n \/\/ \u904d\u5386Result\u5bf9\u8c61\n for (kv <- result.rawCells()) {\n val family = Bytes.toString(kv.getFamilyArray, kv.getFamilyOffset, kv.getFamilyLength)\n val qualifier = Bytes.toString(kv.getQualifierArray, kv.getQualifierOffset, kv.getQualifierLength)\n val value = Bytes.toString(kv.getValueArray, kv.getValueOffset, kv.getValueLength)\n println(\"Family: \" + family + \", Qualifier: \" + qualifier + \", Value: \" + value)\n }\n\n table.close()\n connection.close()\n }\n}\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
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pythonCopy code
from pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom pyspark.sql.functions import *\nfrom pyspark.sql.catalog import Column, Table\n\nspark = SparkSession.builder \\\n .appName(\"HBaseTableCreateExample\") \\\n .getOrCreate()\n\n# HBase\u8fde\u63a5\u914d\u7f6e\nconf = spark.sparkContext._jsc.hadoopConfiguration()\nconf.set(\"hbase.zookeeper.quorum\", \"localhost\")\nconf.set(\"hbase.zookeeper.property.clientPort\", \"2181\")\n\n# \u521b\u5efa\u8868\u683c\u63cf\u8ff0\u7b26\ntable_name = \"students\"\ntable_schema = StructType([\n StructField(\"info:name\", StringType()),\n StructField(\"info:age\", IntegerType()),\n StructField(\"score:math\", IntegerType()),\n StructField(\"score:english\", IntegerType())\n])\ntable_schema_string = ','.join([f\"{col.name} {col.dataType.simpleString()}\" for col in table_schema])\ntable_options = {\"COLUMN_FAMILY_MAP\": \"info,score\"}\n\n# \u521b\u5efa\u8868\u683c\nspark.sql(f\"CREATE TABLE {table_name} USING org.apache.hadoop.hbase.spark \\\n OPTIONS ('catalog', 'hbase', 'schema', '{table_schema_string}', 'table', '{table_name}', \\\n 'newtable', '3', 'COLUMN_FAMILY_MAP', 'info,score')\")\n\nprint(\"Table created\")\nspark.stop()\n<\/code><\/pre>\n\n\n\n\n
pythonCopy code
from pyspark.sql import SparkSession\n\nspark = SparkSession.builder \\\n .appName(\"HBaseDataInsertExample\") \\\n .getOrCreate()\n\n# \u63d2\u5165\u6570\u636e\ndata = [(\"101\", \"John\", 20, 90, 85), (\"102\", \"Jane\", 21, 80, 95), (\"103\", \"Bob\", 19, 70, 75)]\ncolumns = [\"rowkey\", \"info:name\", \"info:age\", \"score:math\", \"score:english\"]\ndf = spark.createDataFrame(data, columns)\ndf.write \\\n .format(\"org.apache.hadoop.hbase.spark\") \\\n .option(\"catalog\", \"hbase\") \\\n .option(\"table\", \"students\") \\\n .option(\"newtable\", \"3\") \\\n .option(\"COLUMN_FAMILY_MAP\", \"info,score\") \\\n .mode(\"append\") \\\n .save()\n\nprint(\"Data inserted\")\nspark.stop()\n<\/code><\/pre>\n\n\n\n\n
pythonCopy code
from pyspark.sql import SparkSession\nfrom pyspark.sql.functions import *\n\nspark = SparkSession.builder \\\n .appName(\"HBaseDataUpdateExample\") \\\n .getOrCreate()\n\n# \u66f4\u65b0\u6570\u636e\ndf = spark.read \\\n .format(\"org.apache.hadoop.hbase.spark\") \\\n .option(\"catalog\", \"hbase\") \\\n .option(\"table\", \"students\") \\\n .option(\"newtable\", \"3\") \\\n .option(\"COLUMN_FAMILY_MAP\", \"info,score\") \\\n .load()\n\ndf = df.withColumn(\"info:age\", when(df[\"rowkey\"] == \"101\", 22).otherwise(df[\"info:age\"]))\n\ndf.write \\\n .format(\"org.apache.hadoop.hbase.spark\") \\\n .option(\"catalog\", \"hbase\") \\\n .option(\"table\", \"students\") \\\n .option(\"newtable\", \"3\") \\\n .option(\"COLUMN_FAMILY_MAP\", \"info,score\") \\\n .mode(\"overwrite\") \\\n .save()\n\nprint(\"Data updated\")\nspark\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
vbnetCopy code
\n4. \u5220\u9664\u6570\u636e\n\n```python\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import *\n\nspark = SparkSession.builder \\\n .appName(\"HBaseDataDeleteExample\") \\\n .getOrCreate()\n\n# \u5220\u9664\u6570\u636e\ndf = spark.read \\\n .format(\"org.apache.hadoop.hbase.spark\") \\\n .option(\"catalog\", \"hbase\") \\\n .option(\"table\", \"students\") \\\n .option(\"newtable\", \"3\") \\\n .option(\"COLUMN_FAMILY_MAP\", \"info,score\") \\\n .load()\n\ndf = df.filter(df[\"rowkey\"] != \"101\")\n\ndf.write \\\n .format(\"org.apache.hadoop.hbase.spark\") \\\n .option(\"catalog\", \"hbase\") \\\n .option(\"table\", \"students\") \\\n .option(\"newtable\", \"3\") \\\n .option(\"COLUMN_FAMILY_MAP\", \"info,score\") \\\n .mode(\"overwrite\") \\\n .save()\n\nprint(\"Data deleted\")\nspark.stop()\n<\/code><\/pre>\n\n\n\n\n
pythonCopy code
from pyspark.sql import SparkSession\n\nspark = SparkSession.builder \\\n .appName(\"HBaseDataQueryExample\") \\\n .getOrCreate()\n\n# \u83b7\u53d6\u6570\u636e\ndf = spark.read \\\n .format(\"org.apache.hadoop.hbase.spark\") \\\n .option(\"catalog\", \"hbase\") \\\n .option(\"table\", \"students\") \\\n .option(\"newtable\", \"3\") \\\n .option(\"COLUMN_FAMILY_MAP\", \"info,score\") \\\n .load()\n\ndf = df.filter(df[\"rowkey\"] == \"101\")\ndf.show()\n\nspark.stop()\n<\/code><\/pre>\n\n\n\norg.apache.hadoop.hbase.spark<\/code><\/p>\n\n\n\n<\/figure>\n\n\n\n
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