When generating JOOQ classes via JOOQ code gen, for each field, there will be a SQLDataType associated with it like below.
public final TableField<EventsRecord, LocalDateTime> CREATED_AT = createField(DSL.name("CREATED_AT"), SQLDataType.LOCALDATETIME(6).nullable(false), this, "");
What's the usage or purpose to have SQLDataType with each generated field? Since we already have a return type and client code is likely to use the this type to do the compile check.
Why we still need to know the actual SQLDataType in generated class/fields?
By client type, you probably mean the LocalDateTime type, i.e. the <T> type that you will find throughout the jOOQ API. Sure, that's the type you care about, but jOOQ, internally, will care about the org.jooq.DataType instead. Your example already gives away two ideas why this may be useful:
There's a precision of 6 fractional digits on LOCALDATETIME(6), which is used (among other things):
In CAST expressions. Try DSL.cast(inline("2000-01-01 00:00:00"), EVENTS.CREATED_AT),
In DDL statements. Try DSLContext.meta(EVENTS). You should see a CREATE TABLE statement with the appropriate data type
In the optimistic locking feature, to create modification timestamps with the right precision.
There's an indication whether the column is nullable, which is used (again among other things):
In DDL statements, see above
In the implicit join feature, to decide whether to produce an INNER JOIN or a LEFT JOIN
There are many other properties a DataType can have, which would be interesting for jOOQ at runtime including:
Custom data type bindings
Character set
Collation
Converters
Default value
Whether it is an identity
Besides, a String is not a String. For example, it could mean CHAR(2), CHAR(5), VARCHAR(100), CLOB, which are all quite different things in some dialects.
It would be a shame if your runtime meta model didn't have this information available.
In Cassandra, when specifying a table and fields, one has to give each field a type (text, int, boolean, etc.). The same applies for collections, you have to give lock a collection to specific type (set<text> and such).
I need to store a list of mixed types in Cassandra. The list may contain numbers, strings and booleans. So I would need something like list<?>.
Is this possible in Cassandra and if not, What workaround would you suggest for storing a list of mixed type items? I sketched a few, but none of them seem the right way to go...
Cassandra's CQL interface is strictly typed, so you will not be able to create a table with an untyped collection column.
I basically see two options:
Create a list field, and convert everything to text (not too nice, I agree)
Use the thift API and store everything as is.
As suggested at http://www.mail-archive.com/user#cassandra.apache.org/msg37103.html I decided to encode the various values into binary and store them into list<blob>. This allows to still query the collection values (in Cassandra 2.1+), one just needs to encode the values in the query.
On python, simplest way is probably to pickle and hexify when storing data:
pickle.dumps('Hello world').encode('hex')
And to load it:
pickle.loads(item.decode('hex'))
Using pickle ties the implementation to python, but it automatically converts to correct type (int, string, boolean, etc.) when loading, so it's convenient.
I am monitoring an application using Zabbix and have defined a custom item which returns a string value. Since my item's values are actually checksums, they will only contain the characters [0-9a-f]. Two mirror copies of my application are running on two servers for the sake of redundancy. I would like to create a trigger which would take the item values from both machines and fire if they are not the same.
For a moment, let's forget about the moment when values change (it's not an atomic operation, so the system may see inconsistent state, which is not a real error, for a short time), since I could work around it by looking at several previous values.
The crux is: how to write a Zabbix trigger expression which could compare for equality the string values of two items (the same item on two mirror hosts, actually)?
Both according to the fine manual and as I confirmed in praxis, the standard operators = and # only work on numeric values, so I can't just write the natural {host1:myitem[param].last(0)} # {host2:myitem[param].last(0)}. Functions such as change() or diff() can only compare values of the same item at different points in time. Functions such as regexp() can only compare the item's value with a constant string/regular expression, not with another item's value. This is very limiting.
I could move the comparison logic into the script which my custom item executes, but it's a bit messy and not elegant, so if at all possible, I would prefer to have this logic inside my Zabbix trigger.
Perhaps despite the limitations listed above, someone can come up with a workaround?
Workaround:
{host1:myitem[param].change(0)} # {host2:myitem[param].change(0)}
When only one of the servers sees a modification since the previously received value, an event is triggered.
From the Zabbix Manual,
change (float, int, str, text, log)
Returns difference between last and previous values.
For strings:
0 - values are equal
1 - values differ
I believe, and am struggling with this EXACT situation to this myself, that the correct way to do this is via calculated items.
You want to create a new ITEM, not trigger (yet!), that performs a calculated comparison on multiple item values (Strings Difference, Numbers within range, etc).
Once you have that item, have the calculation give you a value you can trigger off of. You can use ANY trigger functions in your calculation along with arrhythmic operations.
Now to the issue (which I've submitted a feature request for because this is extremely limiting), most trigger expressions evaluate to a number or a 0/1 bool.
I think I have a solution for my problem, which is that I am tracking a version number from a webpage: e.g. v2.0.1, I believe I can use string connotation and regex in calculated items in order to convert my string values into multiple number values. As these would be a breeze to compare.
But again, this is convoluted and painful.
If you want my advice, have yourself or a dev look at the code for trigger expressions and see if you can submit a patch add one trigger function for simple string comparison. (Difference, Length, Possible conversion to numerical values (using binary and/or hex combinations) etc.)
I'm trying to work on a patch myself, but I don't have time as I have so much monitoring to implement and while zabbix is powerful, it's got several huge flaws. I still believe it's the best monitoring system out there.
Simple answer: Create a UserParameter until someone writes a patch.
You could change your items to return numbers instead of strings. Because your items are checksums that are using only [0-9a-f] characters, they are numbers written in hexadecimal. So you would need to convert the checksum to decimal number.
Because the checksum is a big number, you would need to limit the hexadecimal number to 8 characters for Numeric (unsigned) type before conversion. Or if you would want higher precision, you could use float (but that would be more work):
Numeric (unsigned) - 64bit unsigned integer
Numeric (float) - floating point number
Negative values can be stored.
Allowed range (for MySQL): -999999999999.9999 to 999999999999.9999 (double(16,4)).
I wish Zabbix would have .hashedUnsigned() function that would compute hash of a string and return it as a number. Such a function should be easy to write.
What's the difference between the text data type and the character varying (varchar) data types?
According to the documentation
If character varying is used without length specifier, the type accepts strings of any size. The latter is a PostgreSQL extension.
and
In addition, PostgreSQL provides the text type, which stores strings of any length. Although the type text is not in the SQL standard, several other SQL database management systems have it as well.
So what's the difference?
There is no difference, under the hood it's all varlena (variable length array).
Check this article from Depesz: http://www.depesz.com/index.php/2010/03/02/charx-vs-varcharx-vs-varchar-vs-text/
A couple of highlights:
To sum it all up:
char(n) – takes too much space when dealing with values shorter than n (pads them to n), and can lead to subtle errors because of adding trailing
spaces, plus it is problematic to change the limit
varchar(n) – it's problematic to change the limit in live environment (requires exclusive lock while altering table)
varchar – just like text
text – for me a winner – over (n) data types because it lacks their problems, and over varchar – because it has distinct name
The article does detailed testing to show that the performance of inserts and selects for all 4 data types are similar. It also takes a detailed look at alternate ways on constraining the length when needed. Function based constraints or domains provide the advantage of instant increase of the length constraint, and on the basis that decreasing a string length constraint is rare, depesz concludes that one of them is usually the best choice for a length limit.
As "Character Types" in the documentation points out, varchar(n), char(n), and text are all stored the same way. The only difference is extra cycles are needed to check the length, if one is given, and the extra space and time required if padding is needed for char(n).
However, when you only need to store a single character, there is a slight performance advantage to using the special type "char" (keep the double-quotes — they're part of the type name). You get faster access to the field, and there is no overhead to store the length.
I just made a table of 1,000,000 random "char" chosen from the lower-case alphabet. A query to get a frequency distribution (select count(*), field ... group by field) takes about 650 milliseconds, vs about 760 on the same data using a text field.
(this answer is a Wiki, you can edit - please correct and improve!)
UPDATING BENCHMARKS FOR 2016 (pg9.5+)
And using "pure SQL" benchmarks (without any external script)
use any string_generator with UTF8
main benchmarks:
2.1. INSERT
2.2. SELECT comparing and counting
CREATE FUNCTION string_generator(int DEFAULT 20,int DEFAULT 10) RETURNS text AS $f$
SELECT array_to_string( array_agg(
substring(md5(random()::text),1,$1)||chr( 9824 + (random()*10)::int )
), ' ' ) as s
FROM generate_series(1, $2) i(x);
$f$ LANGUAGE SQL IMMUTABLE;
Prepare specific test (examples)
DROP TABLE IF EXISTS test;
-- CREATE TABLE test ( f varchar(500));
-- CREATE TABLE test ( f text);
CREATE TABLE test ( f text CHECK(char_length(f)<=500) );
Perform a basic test:
INSERT INTO test
SELECT string_generator(20+(random()*(i%11))::int)
FROM generate_series(1, 99000) t(i);
And other tests,
CREATE INDEX q on test (f);
SELECT count(*) FROM (
SELECT substring(f,1,1) || f FROM test WHERE f<'a0' ORDER BY 1 LIMIT 80000
) t;
... And use EXPLAIN ANALYZE.
UPDATED AGAIN 2018 (pg10)
little edit to add 2018's results and reinforce recommendations.
Results in 2016 and 2018
My results, after average, in many machines and many tests: all the same (statistically less than standard deviation).
Recommendation
Use text datatype, avoid old varchar(x) because sometimes it is not a standard, e.g. in CREATE FUNCTION clauses varchar(x)≠varchar(y).
express limits (with same varchar performance!) by with CHECK clause in the CREATE TABLE e.g. CHECK(char_length(x)<=10). With a negligible loss of performance in INSERT/UPDATE you can also to control ranges and string structure e.g. CHECK(char_length(x)>5 AND char_length(x)<=20 AND x LIKE 'Hello%')
On PostgreSQL manual
There is no performance difference among these three types, apart from increased storage space when using the blank-padded type, and a few extra CPU cycles to check the length when storing into a length-constrained column. While character(n) has performance advantages in some other database systems, there is no such advantage in PostgreSQL; in fact character(n) is usually the slowest of the three because of its additional storage costs. In most situations text or character varying should be used instead.
I usually use text
References: http://www.postgresql.org/docs/current/static/datatype-character.html
In my opinion, varchar(n) has it's own advantages. Yes, they all use the same underlying type and all that. But, it should be pointed out that indexes in PostgreSQL has its size limit of 2712 bytes per row.
TL;DR:
If you use text type without a constraint and have indexes on these columns, it is very possible that you hit this limit for some of your columns and get error when you try to insert data but with using varchar(n), you can prevent it.
Some more details: The problem here is that PostgreSQL doesn't give any exceptions when creating indexes for text type or varchar(n) where n is greater than 2712. However, it will give error when a record with compressed size of greater than 2712 is tried to be inserted. It means that you can insert 100.000 character of string which is composed by repetitive characters easily because it will be compressed far below 2712 but you may not be able to insert some string with 4000 characters because the compressed size is greater than 2712 bytes. Using varchar(n) where n is not too much greater than 2712, you're safe from these errors.
text and varchar have different implicit type conversions. The biggest impact that I've noticed is handling of trailing spaces. For example ...
select ' '::char = ' '::varchar, ' '::char = ' '::text, ' '::varchar = ' '::text
returns true, false, true and not true, true, true as you might expect.
Somewhat OT: If you're using Rails, the standard formatting of webpages may be different. For data entry forms text boxes are scrollable, but character varying (Rails string) boxes are one-line. Show views are as long as needed.
A good explanation from http://www.sqlines.com/postgresql/datatypes/text:
The only difference between TEXT and VARCHAR(n) is that you can limit
the maximum length of a VARCHAR column, for example, VARCHAR(255) does
not allow inserting a string more than 255 characters long.
Both TEXT and VARCHAR have the upper limit at 1 Gb, and there is no
performance difference among them (according to the PostgreSQL
documentation).
The difference is between tradition and modern.
Traditionally you were required to specify the width of each table column. If you specify too much width, expensive storage space is wasted, but if you specify too little width, some data will not fit. Then you would resize the column, and had to change a lot of connected software, fix introduced bugs, which is all very cumbersome.
Modern systems allow for unlimited string storage with dynamic storage allocation, so the incidental large string would be stored just fine without much waste of storage of small data items.
While a lot of programming languages have adopted a data type of 'string' with unlimited size, like C#, javascript, java, etc, a database like Oracle did not.
Now that PostgreSQL supports 'text', a lot of programmers are still used to VARCHAR(N), and reason like: yes, text is the same as VARCHAR, except that with VARCHAR you MAY add a limit N, so VARCHAR is more flexible.
You might as well reason, why should we bother using VARCHAR without N, now that we can simplify our life with TEXT?
In my recent years with Oracle, I have used CHAR(N) or VARCHAR(N) on very few occasions. Because Oracle does (did?) not have an unlimited string type, I used for most string columns VARCHAR(2000), where 2000 was at some time the maximum for VARCHAR, and in all practical purposes not much different from 'infinite'.
Now that I am working with PostgreSQL, I see TEXT as real progress. No more emphasis on the VAR feature of the CHAR type. No more emphasis on let's use VARCHAR without N. Besides, typing TEXT saves 3 keystrokes compared to VARCHAR.
Younger colleagues would now grow up without even knowing that in the old days there were no unlimited strings. Just like that in most projects they don't have to know about assembly programming.
I wasted way too much time because of using varchar instead of text for PostgreSQL arrays.
PostgreSQL Array operators do not work with string columns. Refer these links for more details: (https://github.com/rails/rails/issues/13127) and (http://adamsanderson.github.io/railsconf_2013/?full#10).
If you only use TEXT type you can run into issues when using AWS Database Migration Service:
Large objects (LOBs) are used but target LOB columns are not nullable
Due to their unknown and sometimes large size, large objects (LOBs) require more processing
and resources than standard objects. To help with tuning migrations of systems that contain
LOBs, AWS DMS offers the following options
If you are only sticking to PostgreSQL for everything probably you're fine. But if you are going to interact with your db via ODBC or external tools like DMS you should consider using not using TEXT for everything.
character varying(n), varchar(n) - (Both the same). value will be truncated to n characters without raising an error.
character(n), char(n) - (Both the same). fixed-length and will pad with blanks till the end of the length.
text - Unlimited length.
Example:
Table test:
a character(7)
b varchar(7)
insert "ok " to a
insert "ok " to b
We get the results:
a | (a)char_length | b | (b)char_length
----------+----------------+-------+----------------
"ok "| 7 | "ok" | 2
Per the Core Data Programming Guide:
You can specify that an attribute is
optional—that is, it is not required
to have a value. In general, however,
you are discouraged from doing
so—especially for numeric values
(typically you can get better results
using a mandatory attribute with a
default value—in the model—of 0). The
reason for this is that SQL has
special comparison behavior for NULL
that is unlike Objective-C's nil. NULL
in a database is not the same as 0,
and searches for 0 will not match
columns with NULL.
I have always made numeric values non-optional, but have not for dates and strings. It is convenient in my code to base logic on dates and/or strings being nil.
Based on the above recommendations, I am considering making everything in my database non-optional. For dates I could set the model default to a value of 0 and for strings a model default of nothing (""). Then, in my code I could test dates for [date timeIntervalSince1970] != 0 and strings for string.length != 0.
The question is, for a relatively small database, does this really matter from a Core Data performance standpoint? And what is the tradeoff if the attribute in question will never be directly queried via a predicate?
I have not seen any performance problems on small to medium sized data sets. I suspect that this is something you would deal with in the performance stage of your application.
Personally I use the same logic of non numerics being optional if it makes sense as it does indeed make the code easier which in turn gives me more time to optimize later.