If you omit both precision and scale, you can store any precision and scale up to the limit of the precision and scale mentioned above. The following shows the syntax of NUMERIC type with scale zero: NUMERIC(precision) Code language: SQL (Structured Query Language) ( sql ) The scale of the NUMERIC type can be zero or positive. The NUMERIC type can hold a value up to 131,072 digits before the decimal point 16,383 digits after the decimal point. For example, the number 1234.567 has the precision 7 and scale 3. In this syntax, the precision is the total number of digits and the scale is the number of digits in the fraction part. The following illustrate the syntax of the NUMERIC type: NUMERIC(precision, scale) Code language: SQL (Structured Query Language) ( sql ) Typically, you use the NUMERIC type for numbers that require exactness such as monetary amounts or quantities. The NUMERIC type can store numbers with a lot of digits. Introduction to PostgreSQL NUMERIC data type Nevertheless, I somehow get the impression that the DDL has a lot of room for improvement (at least for PostgreSQL).Summary: in this tutorial, you will learn about the PostgreSQL NUMERIC type for storing numeric data. Really nice to see that it also has accounted for the basic business critical aspects like the batch window and batch size. The new history TTL comes to rescue here. Of course, there are ways to optimize that in PostgreSQL (and the system is a pretty slow one in terms of I/O) but it shows the underlying problem quite good. T01:54:42+0200 end_time_ < (CURRENT_TIMESTAMP - '6 months'::interval)ĭeleting ~9M rows took more than 1h 15m!!! ![]() An excerpt from a first execution (basically carried out before the upgrade to 7.7): T01:54:42+0200 CREATE MATERIALIZED VIEW v_cleanup_procinst AS ( Then I wrote myself a simple SQL history cleanup script, executed by a Jenkins with timestamp logging enabled. I also recently tried to delete individual (obsolete) process definitions including their historic data using the cockpit UI and/or the ReST API only to run into timeouts at the JDBC level (which I configured because I don’t want any thread to get stuck for more than 5 minutes. Nevertheless, this could be an option for a version 8.0, no? I totally agree with you on the semantic versioning part. In addition, if the Java code would be adapted as well to use UUID instead of String, the memory footprint would go down a bit as well and benchmarks would be faster anway due to space and CPU-cycle savings in all layers - memory, indexes, storage. I mean you could easily switch to UUID’s completely (drop the default hi-lo generator) and at that time optimize the DDL for the different databases to store it most efficiently (especially the ones that do not know what a UUID is). Question for me now is: is there any reason (other than historical) to stick with simple integer ID’s converted to String? In addition, column ordering does have an impact: database design - Calculating and saving space in PostgreSQL - Stack Overflow Read more on this matter here: PostgreSQL: Documentation: 15: 73.2. TOAST ![]() If I could use the UUID data type of PostgreSQL then the identifier columns would just consume a fixed 16 bytes which are much less in size and will fit into the “plain” row storage area which is much more efficient when scanning than the “extended” area, where all variable-length columns are stored. Looking a bit closer I see that all identifier fields are created as VARCHAR, which are making things much slower than they could be. The main time has been consumed by creating new indexes on history data. Main issue for me here: executing the SQL upgrade scripts took more than 4 hours. I recently did an upgrade from (almost ancient) version 7.4.0 to 7.7.0. My database of choice is PostgreSQL in a replicated high-availability (and read-scalability) setup using pgpool2. ![]() I am using the Camunda BPM in a “clustered” environment using multiple execution nodes (process engine + job executor + BPMN/DMN deployer inside Apache Karaf) and by that - of course - use a UUID generator for providing identifiers.
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