Setup:
Rails 4
MySQL
ThinkingSphinx
I have a model (Record) in my app with almost 500 million rows. This model has 32 fields, but the only two I care about for a particular Sphinx search are name and token. name is what I am searching against using Sphinx, and token is what I want returned to perform other actions in Rails with.
My indices set up is:
ThinkingSphinx::Index.define :records, :with => :real_time do
# fields
indexes name
indexes token
# attributes
has token, as: :token_attr, type: :string
# < several additional attributes >
end
What I want to do is query Sphinx on :records matching against name and have it return distinct token strings in an array.
Here's what I have:
Record.search("red", indices: %w(records), max_matches: num_tokens_i_need, group_by: :token_attr)
... where num_tokens_i_need is generally somewhere in the thousands (less than 10,000)
The above query takes between 5-8 minutes to complete. However, when I simply do:
Record.search("red", indices: %w(records), max_matches: num_tokens_i_need).map(&:token).uniq
The search is incredibly fast (returning several million records in a couple hundred milliseconds), but I don't get back num_tokens_i_need due to the .uniq call.
Basically what I need to do is have a fast Sphinx search which gives me back an exact number of distinct token for a given term (such as "red").
If seeing my sphinx.conf or anything else would be helpful, please let me know.
The Sphinx docs note that grouping is done in memory, so, to get grouped search results, every single document's attributes needs to be in memory at some point. Given there are several million documents in your Record index, I'm guessing this is the cause of the slowness.
Keep in mind that in your second example, millions of records may match your query, but they're not all being returned by Sphinx (and the matching is purely done on fields, attributes aren't involved), which is part of why that query is much faster.
Some thoughts on better ways forward:
If you're just wanting the tokens from Record instances where the name matches exactly, then SQL is probably a better tool for this job. Even with partial matches, using your database's fuzzy matching may be quicker.
If you're just after the number of tokens, rather than the token values, then Sphinx really isn't the right tool for the job. It's not built with aggregation in mind, hence why it's not tuned towards the query you're running.
If the keyword values (in your examples, red) are a known set (rather than user-provided), perhaps you can cache the values and recalculate them on a regular basis (once a day?).
None of these are clear winners, but hopefully they help you find a better solution.
Related
The problem is I need to do pagination.I want to use order by and limit.But my colleague told me mysql will return records in the same order,and since this job doesn't care in which order the records are shown,so we don't need order by.
So I want to ask if what he said is correct? Of course assuming that no records are updated or inserted between the two queries.
You don't show your query here, so I'm going to assume that it's something like the following (where ID is the primary key of the table):
select *
from TABLE
where ID >= :x:
limit 100
If this is the case, then with MySQL you will probably get rows in the same order every time. This is because the only predicate in the query involves the primary key, which is a clustered index for MySQL, so is usually the most efficient way to retrieve.
However, probably may not be good enough for you, and if your actual query is any more complex than this one, probably no longer applies. Even though you may think that nothing changes between queries (ie, no rows inserted or deleted), so you'll get the same optimization plan, that is not true.
For one thing, the block cache will have changed between queries, which may cause the optimizer to choose a different query plan. Or maybe not. But I wouldn't take the word of anyone other than one of the MySQL maintainers that it won't.
Bottom line: use an order by on whatever column(s) you're using to paginate. And if you're paginating by the primary key, that might actually improve your performance.
The key point here is that database engines need to handle potentially large datasets and need to care (a lot!) about performance. MySQL is never going to waste any resource (CPU cycles, memory, whatever) doing an operation that doesn't serve any purpose. Sorting result sets that aren't required to be sorted is a pretty good example of this.
When issuing a given query MySQL will try hard to return the requested data as quick as possible. When you insert a bunch of rows and then run a simple SELECT * FROM my_table query you'll often see that rows come back in the same order than they were inserted. That makes sense because the obvious way to store the rows is to append them as inserted and the obvious way to read them back is from start to end. However, this simplistic scenario won't apply everywhere, every time:
Physical storage changes. You won't just be appending new rows at the end forever. You'll eventually update values, delete rows. At some point, freed disk space will be reused.
Most real-life queries aren't as simple as SELECT * FROM my_table. Query optimizer will try to leverage indices, which can have a different order. Or it may decide that the fastest way to gather the required information is to perform internal sorts (that's typical for GROUP BY queries).
You mention paging. Indeed, I can think of some ways to create a paginator that doesn't require sorted results. For instance, you can assign page numbers in advance and keep them in a hash map or dictionary: items within a page may appear in random locations but paging will be consistent. This is of course pretty suboptimal, it's hard to code and requieres constant updating as data mutates. ORDER BY is basically the easiest way. What you can't do is just base your paginator in the assumption that SQL data sets are ordered sets because they aren't; neither in theory nor in practice.
As an anecdote, I once used a major framework that implemented pagination using the ORDER BY and LIMIT clauses. (I won't say the same because it isn't relevant to the question... well, dammit, it was CakePHP/2). It worked fine when sorting by ID. But it also allowed users to sort by arbitrary columns, which were often not unique, and I once found an item that was being shown in two different pages because the framework was naively sorting by a single non-unique column and that row made its way into both ORDER BY type LIMIT 10 and ORDER BY type LIMIT 10, 10 because both sortings complied with the requested condition.
I'm not very knowledgeable about databases. I would want to retrieve, say the "newest" 10 rows with owner ID matching something, and then perhaps paginate to retrieve the next "newest" 10 rows with that owner, and so on. But say I'm adding more and more rows into a database table -- at some point, would such a query become unbearably slow, or are databases generally good enough that this won't be a worry?
I imagine it would be an issue because to get the "newest" 10 rows you'd have to order by date, which is O(n log n). With this assumption, I sought a possible solution from SQL Server SELECT LAST N Rows.
It pointed me to http://www.sqlservercurry.com/2009/02/retrieve-last-n-rows-based-on-condition.html where I found that there is a PARTITION BY option for a query. I imagine this means first selecting all the rows that match the owner ID, and THEN ordering them, which would be significantly faster, and fast enough to not worry about for most applications. Is this the correct understanding?
Otherwise, is there some better way to get the "newest" N rows ( seems to suggest it is)?
I'm developing the app in Django if anyone knows a convenient way, but otherwise Django also allows raw database queries.
Okay, if you are using django, then you don't have to worry about DB's complexity. ORM is here to resolve your worries.
Simple fact, Django uses lazy query. So, it will reduce your DB hits and improve system performance.
So, according to your initial part of question, you can simply run this query:
queryset = YourModel.objects.filter(**lookup_condition).order_by('id')
It will get a queryset with the objects which match the condition from database of that Model class. For details, check this: https://docs.djangoproject.com/en/1.9/ref/models/querysets/#django.db.models.query.QuerySet.filter
And to paginate over it, run like this:
first_ten_values = queryset[0:9]
second_ten_values = queryset[10:19]
...
Greeting,
My question; Whether or no sql query (SELECT) continues or stops reading data (records) from table when find the value that I was looking for?
referance: "In order to return data for this query, mysql must start at the beginning of the disk data file, read in enough of the record to know where the category field data starts (because long_text is variable length), read this value, see if it satisfies the where condition (and so decide whether to add to the return record set), then figure out where the next record set is, then repeat."
link for referance: http://www.verynoisy.com/sql-indexing-dummies/#how_the_database_finds_records_normally
In general you don't know and you don't care, but you have to adapt when queries take too long to execute. When you do something like
select a,b,c from mytable where a=3 and b=5
then the database engine has a couple of options to optimize. When all these options fail, then it will do a "full table scan" - which means, it will have to examine the entire table to see which rows are eligible. When you have indices on e.g. column a then the database engine can optimize the search because it can pre-select rows where a has value 3. So, in general, make sure that you have indices for the columns that are most searched. (Perversely, some database engines get confused when you have too many indices and will fall back to a full table scan because they've lost their way...)
As to whether or not the scanning stops: In general, the database engine has to examine all data in the table (hopefully aided by indices) and won't stop after having found just one hit. If you want just the first hit, use a limit 1 clause to make sure that your result set has only one outcome. But then again, if you have a sort by clause, the database engine cannot stop after the first hit, there might be next ones that should get priority given the sorting.
Summarizing, how the db engine does its scan depends on how smart it is, what indices are available etc.. If your select queries take too long then consider re-organizing your indices, writing your select statements differently, or rebuilding the table.
The RDBMS reading data from disk is something you cannot know, you should not care and you must not rely on.
The issue is too broad to get a precise answer. The engine reads data from storage in blocks, a block can contain records that are not needed by the query at hand. If all the columns needed by the query is available in an index, the RDBMS won't even read the data file, it will only use the index. The data it needs could already be cached in memory (because it was read during the execution of a previous query). The underlying OS and the storage media also keep their own caches.
On a busy system, all these factors could lead to very different storage access patterns while running the same query several times on a couple of minutes apart.
Yes it scans the entire file. Unless you put something like
select * from user where id=100 limit 1
This of course will still search entire rows if id 100 is the last record.
If id is a primary key it will automatically be indexed and searching would be optimized
I'm sorry... I thought the table.
I will change question and I will explain it in the following image;
I understand that in CASE 1 all columns must be read with each iteration.
My question is: If it's the same in the CASE 2 or columns that are not selected in the query are excluded from reading in each iteration.
Also, are the both queries are the some in performance perspective?
Clarify:
CASE: 1 In first CASE select print all data
CASE: 2 In second CASE select print columns first_name and last_name
Whether in CASE 2 mysql server (SQL query) reads only columns first_name, last_name or read the entire table to get that data(rows)=(first_name, last_name)?
An interest of me how the server reads table row in CASE 1 and CASE 2?
Generally speaking, how are MySQL queries generated from "Advanced Search Forms" with 20 - 30 optional fields optimized when there are so many different possibilities, columns, tables, outcomes, etc.?
Generally speaking, they aren't returned from the database. With advanced searches that have many field options and maybe also some boolean logic thrown in for good measure are returned from a search server such as Lucene, Sphinx or Xapian.
Returning from the database is almost impossible to do efficiently. It is usually done by building query dynamically such as:
SELECT ... WHERE 1=1
Then, loop over each other field and add
AND field LIKE '%abc'
AND field_2 LIKE '%def%'
AND field LIKE 'GHI%'
Idealy adding the whildchar % at the start or end if possible (for performance reasons), or both for searches within the field data.
Building and running this query makes it virtually impossible to have good indexing which makes them slow and a prime candidate for performance bottlenecks.
Let's say, I have :
Key | Indexes | Key-values
----+---------+------------
001 | 100001 | Alex
002 | 100002 | Micheal
003 | 100003 | Daniel
Lets say, we want to search 001, how to do the fast searching process using hash table?
Isn't it the same as we use the "SELECT * from .. " in mysql? I read alot, they say, the "SELECT *" searching from beginning to end, but hash table is not? Why and how?
By using hash table, are we reducing the records we are searching? How?
Can anyone demonstrate how to insert and retrieve hash table process in mysql query code? e.g.,
SELECT * from table1 where hash_value="bla" ...
Another scenario:
If the indexes are like S0001, S0002, T0001, T0002, etc. In mysql i could use:
SELECT * from table WHERE value = S*
isn't it the same and faster?
A simple hash table works by keeping the items on several lists, instead of just one. It uses a very fast and repeatable (i.e. non-random) method to choose which list to keep each item on. So when it is time to find the item again, it repeats that method to discover which list to look in, and then does a normal (slow) linear search in that list.
By dividing the items up into 17 lists, the search becomes 17 times faster, which is a good improvement.
Although of course this is only true if the lists are roughly the same length, so it is important to choose a good method of distributing the items between the lists.
In your example table, the first column is the key, the thing we need to find the item. And lets suppose we will maintain 17 lists. To insert something, we perform an operation on the key called hashing. This just turns the key into a number. It doesn't return a random number, because it must always return the same number for the same key. But at the same time, the numbers must be "spread out" widely.
Then we take the resulting number and use modulus to shrink it down to the size of our list:
Hash(key) % 17
This all happens extremely fast. Our lists are in an array, so:
_lists[Hash(key % 17)].Add(record);
And then later, to find the item using that key:
Record found = _lists[Hash(key % 17)].Find(key);
Note that each list can just be any container type, or a linked list class that you write by hand. When we execute a Find in that list, it works the slow way (examine the key of each record).
Do not worry about what MySQL is doing internally to locate records quickly. The job of a database is to do that sort of thing for you. Just run a SELECT [columns] FROM table WHERE [condition]; query and let the database generate a query plan for you. Note that you don't want to use SELECT *, since if you ever add a column to the table that will break all your old queries that relied on there being a certain number of columns in a certain order.
If you really want to know what's going on under the hood (it's good to know, but do not implement it yourself: that is the purpose of a database!), you need to know what indexes are and how they work. If a table has no index on the columns involved in the WHERE clause, then, as you say, the database will have to search through every row in the table to find the ones matching your condition. But if there is an index, the database will search the index to find the exact location of the rows you want, and jump directly to them. Indexes are usually implemented as B+-trees, a type of search tree that uses very few comparisons to locate a specific element. Searching a B-tree for a specific key is very fast. MySQL is also capable of using hash indexes, but these tend to be slower for database uses. Hash indexes usually only perform well on long keys (character strings especially), since they reduce the size of the key to a fixed hash size. For data types like integers and real numbers, which have a well-defined ordering and fixed length, the easy searchability of a B-tree usually provides better performance.
You might like to look at the chapters in the MySQL manual and PostgreSQL manual on indexing.
http://en.wikipedia.org/wiki/Hash_table
Hash tables may be used as in-memory data structures. Hash tables may also be adopted for use with persistent data structures; database indices sometimes use disk-based data structures based on hash tables, although balanced trees are more popular.
I guess you could use a hash function to get the ID you want to select from. Like
SELECT * FROM table WHERE value = hash_fn(whatever_input_you_build_your_hash_value_from)
Then you don't need to know the id of the row you want to select and can do an exact query. Since you know that the row will always have the same id because of the input you build the hash value form and you can always recreate this id through the hash function.
However this isn't always true depending on the size of the table and the maximum number of hashvalues (you often have "X mod hash-table-size" somewhere in your hash). To take care of this you should have a deterministic strategy you use each time you get two values with the same id. You should check Wikipedia for more info on this strategy, its called collision handling and should be mentioned in the same article as hash-tables.
MySQL probably uses hashtables somewhere because of the O(1) feature norheim.se (up) mentioned.
Hash tables are great for locating entries at O(1) cost where the key (that is used for hashing) is already known. They are in widespread use both in collection libraries and in database engines. You should be able to find plenty of information about them on the internet. Why don't you start with Wikipedia or just do a Google search?
I don't know the details of mysql. If there is a structure in there called "hash table", that would probably be a kind of table that uses hashing for locating the keys. I'm sure someone else will tell you about that. =)
EDIT: (in response to comment)
Ok. I'll try to make a grossly simplified explanation: A hash table is a table where the entries are located based on a function of the key. For instance, say that you want to store info about a set of persons. If you store it in a plain unsorted array, you would need to iterate over the elements in sequence in order to find the entry you are looking for. On average, this will need N/2 comparisons.
If, instead, you put all entries at indexes based on the first character of the persons first name. (A=0, B=1, C=2 etc), you will immediately be able to find the correct entry as long as you know the first name. This is the basic idea. You probably realize that some special handling (rehashing, or allowing lists of entries) is required in order to support multiple entries having the same first letter. If you have a well-dimensioned hash table, you should be able to get straight to the item you are searching for. This means approx one comparison, with the disclaimer of the special handling I just mentioned.