Try out parallel import and data compression

This topic describes parallel import and data compression.

Scenarios where they are applicable include:

• Data migration: When you need to migrate a large amount of data from one system to another, parallel import can significantly speed up the data migration process. Data compression can reduce the required transmission bandwidth and storage resources.

• Backup and restore: Data compression can reduce the size of backup files to save storage space. Parallel import can accelerate the restore process.

• Columnstore tables: When you query data from a columnstore table, you usually need to access only the columns involved in the query, instead of entire rows. Therefore, you can scan and process compressed columns while they are still compressed, which reduces the I/O operations and accelerates the query. This ultimately improves the overall query performance. We recommend that you compress data in columnstore tables after you import the data in batches. Note that the minor compaction in a columnstore table proceeds slowly.

Parallel import

Apart from analytical queries, operational OLAP also involves the parallel import of a large amount of data, namely, batch processing. The parallel execution framework of OceanBase Database allows DML statements to be executed concurrently (Parallel DML), enabling concurrent writes across servers for a multi-node database, while ensuring the consistency of large transactions. The asynchronous minor compaction mechanism optimizes the support for large transactions in memory-limited situations for the LSM-tree-based storage engine.

Try out PDML with this example: Based on the lineitem table of the TPC-H benchmark test, create a new table named lineitem2 with the same schema. Then, execute an INSERT INTO ... SELECT statement to insert all 6 million rows from the lineitem table to the lineitem2 table. Perform the following steps to observe the results with PDML enabled and compare them with those with PDML disabled.

First, copy the schema of the lineitem table to create the lineitem2 table. Note that in OceanBase Database, you can scale out a table by partition. In this example, the lineitem2 table also has 16 partitions:

obclient [test]>  SHOW CREATE TABLE lineitem\G
*************************** 1. row ***************************
       Table: lineitem

Create Table: CREATE TABLE `lineitem` (
  `l_orderkey` bigint(20) NOT NULL,
  `l_partkey` bigint(20) NOT NULL,
  `l_suppkey` bigint(20) NOT NULL,
  `l_linenumber` bigint(20) NOT NULL,
  `l_quantity` bigint(20) NOT NULL,
  `l_extendedprice` bigint(20) NOT NULL,
  `l_discount` bigint(20) NOT NULL,
  `l_tax` bigint(20) NOT NULL,
  `l_returnflag` char(1) DEFAULT NULL,
  `l_linestatus` char(1) DEFAULT NULL,
  `l_shipdate` date NOT NULL,
  `l_commitdate` date DEFAULT NULL,
  `l_receiptdate` date DEFAULT NULL,
  `l_shipinstruct` char(25) DEFAULT NULL,
  `l_shipmode` char(10) DEFAULT NULL,
  `l_comment` varchar(44) DEFAULT NULL,
  PRIMARY KEY (`l_orderkey`, `l_linenumber`),
  KEY `I_L_ORDERKEY` (`l_orderkey`) BLOCK_SIZE 16384 LOCAL,
  KEY `I_L_SHIPDATE` (`l_shipdate`) BLOCK_SIZE 16384 LOCAL
) DEFAULT CHARSET = utf8mb4 ROW_FORMAT = COMPACT COMPRESSION = 'zstd_1.3.8' REPLICA_NUM = 1 BLOCK_SIZE = 16384 USE_BLOOM_FILTER = FALSE TABLET_SIZE = 134217728 PCTFREE = 0 TABLEGROUP = 'x_tpch_tg_lineitem_order_group'
 partition by key(l_orderkey)
  (partition p0,
  partition p1,
  partition p2,
  partition p3,
  partition p4,
  partition p5,
  partition p6,
  partition p7,
  partition p8,
  partition p9,
  partition p10,
  partition p11,
  partition p12,
  partition p13,
  partition p14,
  partition p15)
1 row in set

Insert data without enabling PDML

After the lineitem2 table is created, insert the data without enabling PDML. Since this is a large transaction involving 6 million rows, you need to increase the default transaction timeout period of OceanBase Database (in microseconds):

# SET ob_query_timeout = 1000000000;  
# SET ob_trx_timeout = 1000000000;  

The execution result is as follows:

obclient [test]> INSERT INTO lineitem2 SELECT * FROM lineitem;
Query OK, 6001215 rows affected (1 min 47.312 sec)
Records: 6001215  Duplicates: 0  Warnings: 0

As we can see, it takes 107 seconds for OceanBase Database to insert 6 million rows in a single transaction when PDML is disabled.

Enable PDML

Add a hint to enable PDML. Note that before you insert the data again, you need to clear the previously inserted data.

obclient [test]> TRUNCATE TABLE lineitem2;
obclient [test]> INSERT /*+ parallel(16) enable_parallel_dml */ INTO lineitem2 SELECT * FROM lineitem;

Check the execution time:

obclient> TRUNCATE TABLE lineitem2;
Query OK, 0 rows affected (0.108 sec)

obclient> INSERT /*+ parallel(16) enable_parallel_dml */ INTO lineitem2 SELECT * FROM lineitem;
Query OK, 6001215 rows affected (22.117 sec)
Records: 6001215  Duplicates: 0  Warnings: 0

As we can see, with PDML enabled, OceanBase Database inserts 6 million rows in approximately 22 seconds. Therefore, PDML improves the performance by about 5 times. This feature helps users who need to process batch data.

Data compression

OceanBase Database has developed its own storage engine based on the LSM-tree structure. In this architecture, data is divided into baseline data (SSTable) and incremental data (MemTable). The baseline data is stored in the disk, while incremental modifications are tracked in the memory. This approach not only enables more compact storage of data in the disk but also allows OceanBase Database to achieve a high compression ratio for data stored in the database through re-compression based on general compression algorithms. Importantly, this data compression does not compromise query and write performance. The following describes how to import a large amount of external data and observe the compression ratio of the data in OceanBase Database.

Data preparation

First, use the CreateData.jar tool to generate 50 million rows of simulated data to the /home/soft directory. The data generation process takes about 15 minutes. You can also use other tools to generate test data.

#mkdir /home/soft/
#java -jar CreateData.jar /home/soft/ 50000000
filePath is : /home/soft/
Start Time : 2022-07-10 15:33:00
End Time : 2022-07-10 15:52:53
#du -sh *
10G     t_bigdata_loader.unl

OceanBase Database allows you to import data in CSV format to the database through multiple methods. This topic introduces the method of using the Load Data statement.

1. Name the generated files and verify their sizes.

   # mv t_bigdata_loader.unl t_f1.csv
   # du -sh t_f1.csv
   10G     t_f1.csv
   

2. The content of the t_f1.csv file shows that the random algorithm in the pre-generated CSV file obtained 8 columns of data that correspond to different data types. Therefore, create a table in the tenant, and import the records in the CSV file to the table to try out the data compression feature of OceanBase Database.

   1|1896404182|1980-06-01|2004-10-25 13:30:39|7470.689|33062564.9527|nOLqnBYtnp|BzWYjZjeodtBNzXSMyBduMNzwDPSiVmhVgPJMeEkeAwKBCorzblwovIHDKBsQhbVjQnIdoeTsiLXTNwyuAcuneuNaol|
   2|572083440|2018-11-09|1998-07-11 01:23:28|6891.054|66028434.4013|UzqteeMaHP|vQWbWBXEWgUqUTzqsOSciiOuvWVcZSrlEOQDwDVGmvGRQYWmhCFdEkpsUsqrWEpKtmxSwURHIHxvmlXHUIxmfelYboeGEuScKKqzpuNLryFsStaFTTRqSsVlCngFFjHnEnpaCnWsdwztbiHJyoGkaxrFmyPAmVregfydArrUZsgRqBpQ|
   3|1139841892|2006-10-07|1999-06-26 17:02:22|286.43692|51306547.5055|KJJtylgxkv|BuBdFTBIIFsEPVxsVBRqAnFXSBdtZDgfumUhIx|
   4|1777342512|1982-12-18|2017-11-19 07:56:35|2986.242|85860387.8696|rTkUBWhdPt|JSazOTAmvtCBrINttDwublNJNRFDIiWkHtWZXmWgKHoZCKGqmmETkIcYLXiSgKkoaATNgjvPxVGjeCOODLEWqrQHqowbMjOLOKrtirWEOpUSxiUudZduTCUvZElKzZfggvCBNthwzKJc|
   ....
   

3. In the test tenant, create a database named test and a table named t_f1. For more information, see the creating a tenant topic.

   # obclient -hxxx.xxx.xxx.xxx -P2881 -uroot@test  -Dtest -A -p -c
   
   obclient [test]> CREATE TABLE t_f1(id DECIMAL(10,0),id2 DECIMAL(10,0),id3 DATE,id4 DATE,id5 FLOAT,id6 FLOAT,id7 VARCHAR(30),id8 VARCHAR(300));
   

Data import

You can use the Load Data statement in OceanBase Database to import data. The Load Data statement supports parallel import. Before you import data, perform the following setup. The Load Data statement supports only local data file import on the OBServer node. If you want to import data remotely, you can use the obloader tool of OceanBase Database.

obclient [test]> SET GLOBAL secure_file_priv = "/";
obclient [test]> GRANT FILE ON *.* to username;

After the setup is complete, reconnect the session to take effect. Then set the transaction timeout period of the session again to ensure that it does not time out during execution.

obclient [test]> SET ob_query_timeout=1000000000;
obclient [test]> SET ob_trx_timeout=1000000000;

Run the following statement to import data:

obclient [test]> LOAD DATA /*+ parallel(16) */ INFILE '/home/soft/t_f1.csv' INTO table t_f1 fields TERMINATED BY '\|' LINES TERMINATED BY '\n';

As you can see, with parallel import enabled, it takes about 4 minutes to import 10 GB of data. The CPU parallelism for the tenant in this topic is 16. You can set an appropriate parallelism level based on your configuration. Higher parallelism leads to faster import.

After the import is complete, query the number of records and the occupied space size in the table.

1. Query that the table contains 50 million records.

   obclient [test]> SELECT COUNT(*) FROM t_f1;
   +----------+
   | count(*) |
   +----------+
   | 50000000 |
   +----------+
   

2. Perform a major compaction.

   Log in to the sys tenant of the cluster as the root user and trigger a major compaction to compress the incremental data and baseline data. You can also manually trigger a compaction by using the following method.

   # obclient -h127.0.0.1 -P2881 -uroot@sys  -Doceanbase -A -p -c
   obclient[oceanbase]> ALTER SYSTEM MAJOR FREEZE;
   

3. Query the state of the major compaction. When the following query returns IDLE, the major compaction is complete.

   obclient [oceanbase]> SELECT * FROM oceanbase.CDB_OB_MAJOR_COMPACTION;
   +-----------+---------------------+----------------------------+----------------------+---------------------+----------------------------+----------------------------+--------+----------+--------------+------+
   | TENANT_ID | FROZEN_SCN          | FROZEN_TIME                | GLOBAL_BROADCAST_SCN | LAST_SCN            | LAST_FINISH_TIME           | START_TIME                 | STATUS | IS_ERROR | IS_SUSPENDED | INFO |
   +-----------+---------------------+----------------------------+----------------------+---------------------+----------------------------+----------------------------+--------+----------+--------------+------+
   |         1 | 1679248800404017149 | 2023-03-20 02:00:00.404017 |  1679248800404017149 | 1679248800404017149 | 2023-03-20 02:00:44.035785 | 2023-03-20 02:00:00.442481 | IDLE   | NO       | NO           |      |
   |      1001 | 1679248804191204504 | 2023-03-20 02:00:04.191205 |  1679248804191204504 | 1679248804191204504 | 2023-03-20 02:00:46.094551 | 2023-03-20 02:00:04.218608 | IDLE   | NO       | NO           |      |
   |      1002 | 1679248802450394471 | 2023-03-20 02:00:02.450394 |  1679248802450394471 | 1679248802450394471 | 2023-03-20 02:00:33.818514 | 2023-03-20 02:00:02.484814 | IDLE   | NO       | NO           |      |
   1 row in set
   

4. Query the following statement in the sys tenant to check the data storage usage after the data is imported to OceanBase Database.

   obclient [oceanbase]> select b.table_name,a.svr_ip,data_size/1024/1024/1024 from CDB_OB_TABLET_REPLICAS a,CDB_OB_TABLE_LOCATIONS b where a.tablet_id=b.tablet_id and b.table_name='T_F1';
   +------------+---------------+----------------------------+
   | table_name | svr_ip        | a.data_size/1024/1024/1024 |
   +------------+---------------+----------------------------+
   | t_f1       | xxx.xx.xxx.xx |             6.144531250000 |
   +------------+---------------+----------------------------+
   

The size of the compressed table is approximately 6.145 GB, and the compression ratio is 10/6.145 = 1.62.