How to query big data in oracle

With the advent of the Internet era, the amount of data has increased exponentially. For enterprises, how to efficiently process and analyze these big data has become an important task. As one of the relational databases with the highest market share, Oracle database's ability to support big data queries has become one of the important reasons why many companies choose Oracle.

So, how does Oracle database perform big data queries? This article will introduce in detail three aspects of Oracle partitioning, indexing and analysis functions.

1. Oracle partitioning

When the amount of data reaches tens of millions or higher, using partition tables to store data is a very good choice. Partitioning a table refers to splitting table data into multiple partitions, with each partition serving as an independent data storage unit. Oracle database supports five partitioning methods: by range, by hash, by list, by composite range-list, and by range-hash.

Take range as an example. Suppose there is an orders table that needs to be split into multiple partitions. The range is divided according to the order_date field, and the data within the range is stored in each partition. The code to create a partition table is as follows:

CREATE TABLE orders
(order_id NUMBER(10) NOT NULL,
 order_date DATE NOT NULL,
 customer_id NUMBER(10) NOT NULL,
 amount NUMBER(10,2),
 CONSTRAINT orders_pk PRIMARY KEY (order_id))
PARTITION BY RANGE (order_date)
(
 PARTITION p1 VALUES LESS THAN (TO_DATE('01-Jan-2016', 'DD-MON-YYYY')),
 PARTITION p2 VALUES LESS THAN (TO_DATE('01-Jan-2017', 'DD-MON-YYYY')),
 PARTITION p3 VALUES LESS THAN (TO_DATE('01-Jan-2018', 'DD-MON-YYYY')),
 PARTITION p4 VALUES LESS THAN (TO_DATE('01-Jan-2019', 'DD-MON-YYYY')),
 PARTITION p5 VALUES LESS THAN (MAXVALUE)
);

The above code creates 5 partitions based on the value range of the order_date field, which can be adjusted appropriately according to the actual situation.

2. Index

The index is to extract the key field data in the table and establish a data structure similar to a dictionary, which is used to speed up the search for data when querying. data structure. Oracle supports multiple index types, such as B-tree index, bitmap index, function index, etc.

Take the B-tree index as an example. Suppose there is an orders table partitioned according to the order_date field. Based on this, a B-tree index needs to be created for the order_id field. The code is as follows:

CREATE INDEX orders_idx ON orders(order_id)
LOCAL;

The above code An index named orders_idx is created, and the LOCAL parameter is used to create an independent B-tree index for each partition to facilitate query acceleration.

3. Analytical function

The analytical function is a special function in the Oracle database. It can be used to embed aggregation calculations, integrals, ratios and other operation results in the query results, and summarize the data in groups. Perform more complex statistical calculations. Analytical functions are commonly used in big data analysis, data mining and other fields.

Take the SUM analysis function as an example. Suppose you need to query the sales of each customer whose order_date is 2018 in the orders table. The code is as follows:

SELECT customer_id, SUM(amount) OVER (PARTITION BY customer_id)
FROM orders
WHERE order_date >= '01-Jan-2018' AND order_date < '01-Jan-2019';

The above code uses the SUM analysis function to customer_id Perform group sums for group fields, and use the PARTITION BY statement to specify partitions for the partition table.

In general, Oracle database has good performance and stability in big data query, and supports a variety of partitioning and indexing methods, as well as rich analysis functions, which can meet the big data processing needs of most enterprises. and analyze needs. Of course, during specific use, reasonable choices and configurations need to be made based on data characteristics and task requirements to further improve query efficiency and accuracy.

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