My DB model is as following:
A.id (1 : n) B.ad_id
So in cayenne for object A a I can do a.getBArray() which returns me all the entries from B from this given A entry. Yet I would like to filter on this list, based on the property active = 1.
Obviously I can use Expression.fromString("active = 1") with SelectQuery, but for this approach I can't find how I associate the A instance under which I want to run this query on.
A different approach is to retrieve all entries from a.getBArray() and filter in code searching only those that have active == true, this approach is IMHO inefficient.
Recommendations are mostly appreciated.
Thank you,
Maxim.
-- EDIT:
My current solution to is (object names have been replaced with a & b respectively):
long aId = DataObjectUtils.longPKForObject(db_a_instance);
String bSQL = "select * from b where active = 1 and a_id = " + aId;
SQLTemplate bQuery = new SQLTemplate(B.class, bSQL);
List<B> dbBs = context.performQuery(bQuery);
and I'm asking if there is a better, more elegent solution?
Thanks.
I've asked similar question on Cayenne's friendly mailing list. You can see here.
It seemed to be the preferred approach is to go via relationship and filter in Java unless the relationship returns very large data. The benefit of doing so that the full list will be in memory and next time when you use relationship, you don't need to make a trip to DB.
The answer is quoted here
Both require a trip to the DB.
(the traversing in relationship approach) requires a trip one time to the DB to fault the groups from the DB, but then it'll be in memory.
(the query with filter approach) requires a trip to the DB every time, so that could be slower in the long run even though it returns fewer matches.
If this is something that only happens once and you are REALLY
concerned about performance (and possibly have a LOT of groups), I'd
go with #2, otherwise #1. You can optimize #1 a bit, too, so you
don't have to iterate each time to check.
via: Michael Gentry
Related
How can I select all items within a given date range?
SELECT * FROM GameScores where createdAt >= start_date && createAt <=end_date
I want to make a query like this. Do I need to crate a global secondary index or not?
I've tried this
public void getItemsByDate(Date start, Date end) {
SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'Z'");
String stringStart = df.format(start);
String stringEnd = df.format(end);
ScanSpec scanSpec = new ScanSpec();
scanSpec.withFilterExpression("CreatedAt BETWEEN :from AND :to")
.withValueMap(
new ValueMap()
.withString(":from", stringStart)
.withString(":to", stringEnd));
ItemCollection<ScanOutcome> items = null;
items = gamesScoresTable.scan(scanSpec);
}
But it doesn't work, I'm getting less results than expected.
I can answer your questions, but to suggest any real solution, I would need to see the general shape of your data, as well as what your GameScore's primary key is.
TLDR;
Setup your table so that you can retrieve data with queries, rather than scans and filters, and then create indexes to support lesser used access patterns and improve querying flexibility. Because of how fast reads are when providing the full (or, although not as fast, partial) primary key, i.e. using queries, DynamoDB is optimal when table structure is driven by the application's access patterns.
When designing your tables, keep in mind NoSQL design best practices, as well as best practices for querying and scanning and it will pay dividends in the long run.
Explanations
Question 1
How can I select all items within a given date range?
To answer this, I'd like to break that question down a little more. Let's start with: How can I select all items?
This, you have already accomplished. A scan is a great way to retrieve all items in your table, and unless you have all your items within one partition, it is the only way to retrieve all the items in your table. Scans can be helpful when you have to access data by unknown keys.
Scans, however, have limitations, and as your table grows in size they'll cost you in both performance and dollars. A single scan can only retrieve a maximum of 1MB of data, of a single partition, and is capped at that partition's read capacity. When a scan tops out at either limitation, consecutive scans will happen sequentially. Meaning a scan on a large table could take multiple round trips.
On top of that, with scans you consume read capacity based on the size of the item, no matter how much (or little) data is returned. If you only request a small amount of attributes in your ProjectionExpression, and your FilterExpression eliminates 90% of the items in your table, you still paid to read the entire table.
You can optimize performance of scans using Parallel Scans, but if you require an entire table scan for an access pattern that happens frequently for your application, you should consider restructuring your table. More about scans.
Let's now look at: How can I select all items, based on some criteria?
The ideal way to accomplish retrieving data based on some criteria (in your case SELECT * FROM GameScores where createdAt >= start_date && createAt <=end_date) would be to query the base table (or index). To do so, per the documentation:
You must provide the name of the partition key attribute and a single value for that attribute. Query returns all items with that partition key value.
Like the documentation says, querying a partition will return all of its values. If your GameScores table has a partition key of GameName, then a query for GameName = PacMan will return all Items with that partition key. Other GameName partitions, however, will not be captured in this query.
If you need more depth in your query:
Optionally, you can provide a sort key attribute and use a comparison operator to refine the search results.
Here's a list of all the possible comparison operators you can use with your sort key. This is where you can leverage a between comparison operator in the KeyConditionExpression of your query operation. Something like: GameName = PacMan AND createdAt BETWEEN time1 AND time2 will work, if createdAt is the sort key of the table or index that you are querying.
If it is not the sort key, you might have the answer to your second question.
Question 2
Do I need to create a Global Secondary Index?
Let's start with: Do I need to create an index?
If your base table data structure does not fit some amount of access patterns for your application, you might need to. However, in DynamoDB, the denormalization of data also support more access patterns. I would recommend watching this video on how to structure your data.
Moving onto: Do I need to create a GSI?
GSIs do not support strong read consistency, so if you need that, you'll need to go with a Local Secondary Index (LSI). However, if you've already created your base table, you won't be able to create an LSI. Another difference between the two is the primary key: a GSI can have a different partition and sort key as the base table, while an LSI will only be able to differ in sort key. More about indexes.
I will be having a database table with a few million entries, eg products of an online shop.
If one is out of stock, I want to mark it somehow, and I want to exclude it from any findAll() sql fetches.
Therefore I though one of the following options:
each product already has an integer count of availability. I anyhow have to set that = 0. select * from products where availcount > 0
or I could introduce a boolean available = 'true' field that I set to false if out of stock, and the query would then be ...where available = 'true'
Question: will this make any difference? Are there reasons one of these options should be preferred?
I would stick with the stock levels (int availcount). Bit fields are typically very difficult to index, unless there is a massive skew in the data such that there are of the order of 1% or less products out of stock (and since you will likely be searching for in-stock products only, any index on the flag will be unused).
Since it seems you already store the actual stock level in any event, not storing available in stock indicator will save you headaches on trying to keep the two columns in synch.
Finally, many RDBMS's allow you to add COMPUTED columns (or failing which, add the available indicator to a VIEW), which will allow you the logical derivation of available indicator from the actual availcount, without any storage overhead.
Edit
As per the comments below, note that an index on availcount (for queries WHERE availcount = 0 and availcount > 0) will be equally un-SARGable as an index on a bit field, although an index may not be needed if the products are generally searched by other criteria.
In addition to deriving is available in stock ? in the database, this determination can also be taken in code, e.g. an additional bool isAvailable() { return availcount > 0 ;} method on your entity class.
If you already have the availcount column anyway, there is no reason to add a new one, your availcount > 0 will do.
If you do not need the count for other reasons, and are just trying to decide between having a count or a boolean, consider how hard it is going to be to update that column rather than filtering.
If you only have a boolean, you'll only need to touch when the product goes out of stock (or comes back in). Having a count is more complex: you'd need to update it every time a sale is made or the item is restocked. This is more complicated, has possible performance implications, and a bunch or corner cases to care about. So, unless you need the count for other purposes, it's, probably, a better idea to stick with the boolean.
I think the two options would be equally efficient on SELECT as long as there's an index in the column in question.
Indexing availcount will have a small penalty on any update of this column (and I guess this column will be updated often). On the other hand, having an available column will add redundancy to your database (i.e. it will not be normalized) which you may want to avoid.
I have a table which I need to query, then organize the returned objects into two different lists based on a column value. I can either query the table once, retrieving the column by which I would differentiate the objects and arrange them by looping through the result set, or I can query twice with two different conditions and avoid the sorting process. Which method is generally better practice?
MY_TABLE
NAME AGE TYPE
John 25 A
Sarah 30 B
Rick 22 A
Susan 43 B
Either SELECT * FROM MY_TABLE, then sort in code based on returned types, or
SELECT NAME, AGE FROM MY_TABLE WHERE TYPE = 'A' followed by
SELECT NAME, AGE FROM MY_TABLE WHERE TYPE = 'B'
Logically, a DB query from a Java code will be more expensive than a loop within the code because querying the DB involves several steps such as connecting to DB, creating the SQL query, firing the query and getting the results back.
Besides, something can go wrong between firing the first and second query.
With an optimized single query and looping with the code, you can save a lot of time than firing two queries.
In your case, you can sort in the query itself if it helps:
SELECT * FROM MY_TABLE ORDER BY TYPE
In future if there are more types added to your table, you need not fire an additional query to retrieve it.
It is heavily dependant on the context. If each list is really huge, I would let the database to the hard part of the job with 2 queries. At the opposite, in a web application using a farm of application servers and a central database I would use one single query.
For the general use case, IMHO, I will save database resource because it is a current point of congestion and use only only query.
The only objective argument I can find is that the splitting of the list occurs in memory with a hyper simple algorithm and in a single JVM, where each query requires a bit of initialization and may involve disk access or loading of index pages.
In general, one query performs better.
Also, with issuing two queries you can potentially get inconsistent results (which may be fixed with higher transaction isolation level though ).
In any case I believe you still need to iterate through resultset (either directly or by using framework's methods that return collections).
From the database point of view, you optimally have exactly one statement that fetches exactly everything you need and nothing else. Therefore, your first option is better. But don't generalize that answer in way that makes you query more data than needed. It's a common mistake for beginners to select all rows from a table (no where clause) and do the filtering in code instead of letting the database do its job.
It also depends on your dataset volume, for instance if you have a large data set, doing a select * without any condition might take some time, but if you have an index on your 'TYPE' column, then adding a where clause will reduce the time taken to execute the query. If you are dealing with a small data set, then doing a select * followed with your logic in the java code is a better approach
There are four main bottlenecks involved in querying a database.
The query itself - how long the query takes to execute on the server depends on indexes, table sizes etc.
The data volume of the results - there could be hundreds of columns or huge fields and all this data must be serialised and transported across the network to your client.
The processing of the data - java must walk the query results gathering the data it wants.
Maintaining the query - it takes manpower to maintain queries, simple ones cost little but complex ones can be a nightmare.
By careful consideration it should be possible to work out a balance between all four of these factors - it is unlikely that you will get the right answer without doing so.
You can query by two conditions:
SELECT * FROM MY_TABLE WHERE TYPE = 'A' OR TYPE = 'B'
This will do both for you at once, and if you want them sorted, you could do the same, but just add an order by keyword:
SELECT * FROM MY_TABLE WHERE TYPE = 'A' OR TYPE = 'B' ORDER BY TYPE ASC
This will sort the results by type, in ascending order.
EDIT:
I didn't notice that originally you wanted two different lists. In that case, you could just do this query, and then find the index where the type changes from 'A' to 'B' and copy the data into two arrays.
I have 2 sets of data.
Let say one is a people, another is a group.
A people can be in multiple groups while a group can have multiple people.
My operations will basically be CRUD on group and people.
As well as a method that makes sure a list of people are in different groups (which gets called alot).
Right now I'm thinking of making a table of binary 0's and 1's with horizontally representing all the people and vertically all the groups.
I can perform the method in O(n) time by adding each list of binaries and compare with the "and" operation of the list of binaries.
E.g
Group A B C D
ppl1 1 0 0 1
ppl2 0 1 1 0
ppl3 0 0 1 0
ppl4 0 1 0 0
check (ppl1, ppl2) = (1001 + 0110) == (1001 & 0110)
= 1111 == 1111
= true
check (ppl2, ppl3) = (0110 + 0010) == (0110+0010)
= 1000 ==0110
= false
I'm wondering if there is a data structure that does something similar already so I don't have to write my own and maintain O(n) runtime.
I don't know all of the details of your problem, but my gut instinct is that you may be over thinking things here. How many objects are you planning on storing in this data structure? If you have really large amounts of data to store here, I would recommend that you use an actual database instead of a data structure. The type of operations you are describing here are classical examples of things that relational databases are good at. MySQL and PostgreSQL are examples of large scale relational databases that could do this sort of thing in their sleep. If you'd like something lighter-weight SQLite would probably be of interest.
If you do not have large amounts of data that you need to store in this data structure, I'd recommend keeping it simple, and only optimizing it when you are sure that it won't be fast enough for what you need to do. As a first shot, I'd just recommend using java's built in List interface to store your people and a Map to store groups. You could do something like this:
// Use a list to keep track of People
List<Person> myPeople = new ArrayList<Person>();
Person steve = new Person("Steve");
myPeople.add(steve);
myPeople.add(new Person("Bob"));
// Use a Map to track Groups
Map<String, List<Person>> groups = new HashMap<String, List<Person>>();
groups.put("Everybody", myPeople);
groups.put("Developers", Arrays.asList(steve));
// Does a group contain everybody?
groups.get("Everybody").containsAll(myPeople); // returns true
groups.get("Developers").containsAll(myPeople); // returns false
This definitly isn't the fastest option available, but if you do not have a huge number of People to keep track of, you probably won't even notice any performance issues. If you do have some special conditions that would make the speed of using regular Lists and Maps unfeasible, please post them and we can make suggestions based on those.
EDIT:
After reading your comments, it appears that I misread your issue on the first run through. It looks like you're not so much interested in mapping groups to people, but instead mapping people to groups. What you probably want is something more like this:
Map<Person, List<String>> associations = new HashMap<Person, List<String>>();
Person steve = new Person("Steve");
Person ed = new Person("Ed");
associations.put(steve, Arrays.asList("Everybody", "Developers"));
associations.put(ed, Arrays.asList("Everybody"));
// This is the tricky part
boolean sharesGroups = checkForSharedGroups(associations, Arrays.asList(steve, ed));
So how do you implement the checkForSharedGroups method? In your case, since the numbers surrounding this are pretty low, I'd just try out the naive method and go from there.
public boolean checkForSharedGroups(
Map<Person, List<String>> associations,
List<Person> peopleToCheck){
List<String> groupsThatHaveMembers = new ArrayList<String>();
for(Person p : peopleToCheck){
List<String> groups = associations.get(p);
for(String s : groups){
if(groupsThatHaveMembers.contains(s)){
// We've already seen this group, so we can return
return false;
} else {
groupsThatHaveMembers.add(s);
}
}
}
// If we've made it to this point, nobody shares any groups.
return true;
}
This method probably doesn't have great performance on large datasets, but it is very easy to understand. Because it's encapsulated in it's own method, it should also be easy to update if it turns out you need better performance. If you do need to increase performance, I would look at overriding the equals method of Person, which would make lookups in the associations map faster. From there you could also look at a custom type instead of String for groups, also with an overridden equals method. This would considerably speed up the contains method used above.
The reason why I'm not too concerned about performance is that the numbers you've mentioned aren't really that big as far as algorithms are concerned. Because this method returns as soon as it finds two matching groups, in the very worse case you will call ArrayList.contains a number of times equal to the number of groups that exist. In the very best case scenario, it only needs to be called twice. Performance will likely only be an issue if you call the checkForSharedGroups very, very often, in which case you might be better off finding a way to call it less often instead of optimizing the method itself.
Have you considered a HashTable? If you know all of the keys you'll be using, it's possible to use a Perfect Hash Function which will allow you to achieve constant time.
How about having two separate entities for People and Group. Inside People have a set of Group and vice versa.
class People{
Set<Group> groups;
//API for addGroup, getGroup
}
class Group{
Set<People> people;
//API for addPeople,getPeople
}
check(People p1, People p2):
1) call getGroup on both p1,p2
2) check the size of both the set,
3) iterate over the smaller set, and check if that group is present in other set(of group)
Now, you can basically store People object in any data structure. Preferably a linked list if size is not fixed otherwise an array.
I need to get an equivalent to this SQL that can be run using Hibernate. It doesn't work as is due to special characters like #.
SELECT place from (select #curRow := #curRow + 1 AS place, time, id FROM `testing`.`competitor` JOIN (SELECT #curRow := 0) r order by time) competitorList where competitorList.id=4;
My application is managing results of running competitions. The above query is selecting for a specific competitor, it's place based on his/her overall time.
For simplicity I'll only list the COMPETITOR table structure (only the relevant fields). My actual query involves a few joins, but they are not relevant for the question:
CREATE TABLE competitor {
id INT,
name VARCHAR,
time INT
}
Note that competitors are not already ordered by time, thus, the ID cannot be used as rank. As well, it is possible to have two competitors with the same overall time.
Any idea how I could make this work with Hibernate?
Hard to tell without a schema, but you may be able to use something like
SELECT COUNT(*) FROM testing ts
WHERE ts.score < $obj.score
where I am using the $ to stand for whatever Hibernate notation you need to refer to the live object.
I couldn't find any way to do this, so I had to change the way I'm calculating the position. I'm now taking the top results and am creating the ladder in Java, rather than in the SQL query.