Data Lake (11): Iceberg table data organization and query

List of articles

Iceberg Table data organization and query

One 、 download avro-tools jar package

Two 、​​​​​​​ stay Hive Created in Iceberg Table and insert data

3、 ... and 、​​​​​​​​​​​​​​ see Iceberg Underlying data storage

1、 Query the latest snapshot data

2、 Query the data of a snapshot

3、 View the data of a snapshot based on the timestamp

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Iceberg Table data organization and query

One 、 download avro-tools jar package

Because you need to check later avro The contents of the document , We can go through avro-tool.jar Check it out. avro The data content . It can be downloaded from the following website avro-tools Corresponding jar package , Download and upload to node5 Node ：

“https://mvnrepository.com/artifact/org.apache.avro/avro-tools”.

see avro The following commands can be directly executed for file information , Can be avro Convert the data in to the corresponding json data .

[[email protected] ~]# java -jar /software/avro-tools-1.8.1.jar tojson snap-*-wqer.avro

Two 、​​​​​​​ stay Hive Created in Iceberg Table and insert data

stay Hive Created in Iceberg Format table , And insert the following data ：

# stay Hive Created in iceberg Format table 
create table test_iceberg_tbl1(
id int ,
name string,
age int) 
partitioned by (dt string) 
stored by 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler';

# Insert the following data 
insert into test_iceberg_tbl1 values (1,"zs",21,"20211212");
insert into test_iceberg_tbl1 values (2,"ls",22,"20211212");
insert into test_iceberg_tbl1 values (3,"ww",23,"20211213");
insert into test_iceberg_tbl1 values (4,"ml",24,"20211213");
insert into test_iceberg_tbl1 values (5,"tq",25,"20211213");

3、 ... and 、​​​​​​​​​​​​​​ see Iceberg Underlying data storage

The following figure for Iceberg surface “test_iceberg_tbl1” stay HDFS Data organization chart stored in ：

 

  From the above figure, we can see that there are 5 individual Snapshot snapshot , above 5 individual Snapshot In fact, it corresponds to 5 individual Manifest list List of checklists .

1、 Query the latest snapshot data

In order to understand Iceberg How to query the latest data , You can refer to the following figure to understand the underlying implementation in detail .

Inquire about Iceberg Table data , First get the latest metadata Information , Get it here first “00000-*ec504.metadata.json” Metadata information , Parsing the current metadata file can get a snapshot of the current table id:“949358624197301886” And all the snapshot information of this table , That is to say json In information snapshots The value of the array . According to the snapshot of the current table id Value to get the corresponding snapshot Corresponding avro file information ：“snap-*-32800.avro”, We can find the path corresponding to the current snapshot , See what it contains Manifest The manifest file has 5 individual ："*32800-m0.avro"、"*2abba-m0.avro"、"*d33de-m0.avro"、"*748bf-m0.avro"、"*b946e-m0.avro", Read the Iceberg The latest data of the format table is to read the corresponding data described in these files parquet Data file is enough .

We can see “snap-*-32800.avro” The snapshot file contains not only manifest Path information , also “added_data_files_count”、“existing_data_files_count”、“deleted_data_files_count” Three attributes ,Iceberg according to deleted_data_files_count Greater than 0 To determine the corresponding manifest Is there any deleted data in the manifest file , If one manifest The value in the manifest file is greater than 0 Represents data deletion , You don't need to read this when reading data manifest The data file corresponding to the manifest file .

according to Manifest list The corresponding manifest Inventory file , Each document describes the corresponding parquet Location information of file storage , You can see in the corresponding avro In file “status” attribute , The attribute is 1 For the corresponding parquet The file is a new file , Read required , by 2 representative parquet File deleted .

2、 Query the data of a snapshot

Apache Iceberg Supports querying snapshots at any time in history , When querying, you need to specify snapshot-id Attribute is enough , This can only be done through Spark/Flink To query and implement , For example, in Spark Query a snapshot data in as follows ：

spark.read.option("snapshot-id",6155408340798912701L).format("iceberg").load("path")

  The principle of querying a snapshot data is shown in the following figure （ To query the snapshot id by “6155408340798912701” As an example ）：

  As can be seen from the figure above , In fact, the difference between reading historical snapshot data and reading the latest data is found snapshot-id It's just different , The principle is the same .

3、 View the data of a snapshot based on the timestamp

Apache iceberg It also supports the adoption of as-of-timestamp Parameter execution timestamp to read the data of a snapshot , Also through Spark/Flink To read ,Spark Read the code as follows ：

spark.read.option("as-of-timestamp"," Time stamp ").format("iceberg").load("path")

In fact, the principle and method of finding the corresponding data file through timestamp snapshot-id The principle of finding data files is the same , stay *.metadata.json In file , Except for “current-snapshot-id”、“snapshots” In addition to attributes, there are “snapshot-log” attribute , The corresponding values of this attribute are as follows ：

We can see one of them timestamp-ms Properties and snapshot-id attribute , And according to timestamp-ms ascend . stay Iceberg Internal implementation , It will be as-of-timestamp Designated time and snapshot-log Of each element in the array timestamp-ms Compare , Find the last satisfaction timestamp-ms <= as-of-timestamp Corresponding snapshot-id, The principle of same , adopt snapshot-id Then find the data file to read .

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