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Hive timestamps are an interpret to be time-zone less and stored as an offset in UNIX format. It is useful to get the updated information of tables at different internal changes to happen in the table. The hive timestamp format is YEAR-Month-Day-Hours-Minutes-seconds [YYYY-MM-DD HH:MM:SS] with an optional fraction of seconds. Anything else that will come with the above format should be read as a string and converted later.
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In the hive, the timestamp plays a vital role in different applications like transactions to identify the changes or updates on the hive tables.
Note: The hive timestamp is only available with starting Hive 0.8.0 version.
Note: In hive timestamp, we need to use the different timestamp or date function available in the hive and use it as per our requirement.How does TimeStamp Work in Hive?
In the hive timestamp, it has the inbuilt functionality of timestamp, with the help of these predefined functions. We can work on the hive timestamp. It has the functionality to convert the hive timestamp to UNIX time format or form UNIX time format to hive timestamp. All the existing date time user definition functions like a month, day, year, hour, etc. are working with the TIMESTAMP data type.
The hive timestamp support below three conversions:
Integer numeric types: In integer numeric, we can interpret the UNIX timestamp in seconds
Floating-point numeric types: In floating-point numeric, we can interpret the UNIX timestamp in seconds with decimal format or precision
Strings type: In string type, we can interpret the JDBC compliant java.sql.Timestamp format “YYYY-MM-DD HH:MM: SS.fffffffff”. it considers 9 decimal place precision.
Note: The unix_timestamp() function is available in the hive. But it is providing the current timestamp in the second’s format. But the value is not fixed. Hence it is very difficult for optimizing the query and gets the related information. That’s why the unix_timestamp() function will deprecate from the version of hive 2.0.Examples of HiveTimestamp
Following are the example are given below:
As discussed previously, we have the number of predefined functions available in the hive. Let’s explore one by one.1. unix_timestamp()
The UNIX timestamp function is helpful to get the timestamp information in the second format. It is generally useful to calculate the time difference with a different application in Hadoop like YARN, HBase, etc.
Output:2. unix_timestamp(string date)
The UNIX timestamp with string date function helps convert the human-readable time or string date into seconds. It considers the local time zone (the time zone is using by the cluster or node). The string format should be in “YYYY – MM – DD – HH – MM – SS”.
The return data type of unix_timestampfunction is “bigint”.
select unix_timestamp('2023-03-25 16:32:01');
Output:3. unix_timestamp(string date, string pattern)
The UNIX timestamp with string date and patter function is helpful to convert the timestamp in different patterns. As per our requirement, we can get the timestamp in seconds as per “YYYY – MM – DD” or “HH: MM”.
The return data type the unix_timestamp function is “bigint”.
Query – “YYYY – MM – DD” Format
Query – “HH: MM” Format
Output:4. to_date(string timestamp)
In the above function, we can get the complete date information form the timestamp. The timestamp format should in hive timestamp only.
The return data type of to_date function varies from the version of the hive. Suppose the version is below 2.1.0 then its string. If the version is above 2.1.0, then the data type will be “date”.
select to_date('2023-03-25 16:32:01');
Output:5. year(string date)
With the help of the year function, we can fetch the year form string date. The return data type the year function is “int”.
select quarter('2023-03-25 16:32:01');
In the above function, we can fetch the month information form the timestamp. The return data type the monthfunction is “int”.
select month(‘2023-03-25 16:32:01’);
Output:8. hour(string date)
In the above function, we can fetch the hour information form the timestamp.
The return data type the hourfunction is “int”.
select hour('2023-03-25 16:32:01');
Output:9. minute(string date)
In the above function, we can fetch the minute information form the timestamp.
The return data type the minutefunction is “int”.
select minute('2023-03-25 16:32:01');
Output:10. weekofyear(string date)
The return data type the weekofyear function is “int”.
select weekofyear('2023-03-25 16:32:01');
Output:11. datediff(string enddate, string startdate)
With the help of datediff function, we will get the difference between two dates. The output would be in numeric format. The return data type the datediff function is “int”.
select datediff('2023-03-30', '2023-03-25');
In the date_add function, we will get the next date with the respective integer values. We have provided in terms of days. The return data type the date_addfunction is “int”.
select date_add('2023-03-25 16:32:01', 1);
In the date_sub function, we will get the past date value for days integer value.
The return data type the date_sub function is “int”.
select date_sub('2023-03-25 16:32:01', 1);
Output:Advantages of using Hive Timestamp
The hive timestamp is useful to write the automation jobs.
Checks the updated time while inserting the record in the table.
Useful to comparison the timestamp with different services of Hadoop like YARN, HBase and other services.
Very useful in transaction applications, retail industry, etc.
It is helpful in troubleshooting and fixes the hive transactions jobs.Conclusion
We have seen the uncut concept of “Hive Timestamp” with the proper example, explanation, syntax, SQL Query and different functions. With the help of “Hive Timestamp”, we will get the updated information of the hive’s table and current environment. Majorly it will use in automation script, transaction application, retail industry, etc.Recommended Articles
This is a guide to Hive TimeStamp. Here we also discuss the Introduction, and how does timestamp work in a hive? Along with different examples and code implementation. You may also have a look at the following articles to learn more –
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