I am creating an application which uses both MySql and Neo4j. I think that listing the many nodes properties in a table will be faster at reading all those after querying for a specific set of nodes (or even before), but I am open to be proven wrong. After all finding properties of a row is what relational dbs are for.
To ensure consistency, I have created a property on each node which is the auto_increment ID in my sql table.
I wish neo4j would allow indexing a property regardless of labels but that's not the case and I struggle to understand why this is not possible at all.
Question is: do you think that the performance in neo4j would be much better if the index is on a number versus a string? I am thinking whether to drop the numeric id and just stick with node.name
You can configure indexes on properties without referring to particular labels. You do this by editing node_auto_indexing in conf/neo4j.properties.
If you're looking to compare simple equality, I'd guess that indexing on numbers might be slightly faster, but I doubt the difference is big enough to be very meaningful, unless the string alternatives are very large.
Another option would be to put an AutoInc label and index on that label with your auto_id node property.
Assuming that auto_id is the property you added to all nodes to reference the MySQL auto_increment ID column, then:
CREATE INDEX ON AutoInc:(auto_id)
MATCH(n)
SET n :AutoInc
Related
Suppose I have a database with several columns. In each column there are lots of values that are often similar.
For example I can have a column with the name "Description" and a value could be "This is the description for the measurement". This description can occur up to 1000000 times in this column.
My question is not how I could optimize the design of this database but how a database handles such redundant values. Are these redundant values stored as effectively as with a perfect design (with respect to the total size of the database)? If so, how are the values compressed?
The only correct answer would be: depends on the database and the configuration. Because there is no silver bullet for this one. Some databases do only store values of each column once (some column stores or the like) but technically there is no necessity to do or not do this.
In some databases you can let the DBMS propose optimizations and in such a case it could possibly propose an ENUM field that holds only existing values, which would reduce the string to an id that references the string. This "optimization" comes at a price, for example, when you want to add a new value in the field description you have to adapt the ENUM field.
Depending on the actual use case those optimizations are worth nothing or are even a show stopper, for example when the data changes very often (inserts or updates). The dbms would spend more time managing uniqueness/duplicates than actually processing queries.
On the question of compression: also depends on the configuration and the database system I guess, depends on the field type too. text data can be compressed and in the case of non-indexed text fields there should be almost no drawback in using a simple compression algorithm. Which algorithm depends on the dbms and configuration, I suspect.
Unless you become more specific, there is no more specific answer, I believe.
I have a tagging system on my website. Trying to figure out whether its better to have a master tag table and another table with references to the master table, or a table with each row containing the text of the tag.
Thoughts on what would scale up better?
Thanks.
Scaling up isn't your problem, it's efficiency.
It is significantly easier for the server to search for the integer value 42 than the string value "answer", for example. It also takes up less space in storage, especially in the index table. You can then look up or join on the tag name table (I would favour looking it up separately, because then you can use Memcache or similar to store the names for even faster access)
I'm going to create a table which will have an amount of rows between 1000-20000, and I'm having fields that might repeat a lot... about 60% of the rows will have this value, where about each 50-100 have a shared value.
I've been concerned about efficiency lately and I'm wondering whether it would be better to store this string on each row (it would be between 8-20 characters) or to create another table and link them with its representative ID instead... So having ~1-50 rows in this table replacing about 300-5000 strings with ints?
Is this a good approach, or at all even neccessary?
Yes, it's a good approach in most circumstances. It's called normalisation, and is mainly done for two reasons:
Removing repeated data
Avoiding repeating entities
I can't tell from your question what the reason would be in your case.
The difference between the two is that the first reuses values that just happen to look the same, while the second connects values that have the same meaning. The practical difference is in what should happen if a value changes, i.e. if the value changes for one record, should the value itself change so that it changes for all other records also using it, or should that record be connected to a new value so that the other records are left unchanged.
If it's for the first reason then you will save space in the database, but it will be more complicated to update records. If it's for the second reason you will not only save space, but you will also reduce the risk of inconsistency, as a value is only stored in one place.
That is a good approach to have a look-up table for the strings. That way you can build more efficient indexes on the integer values. It wouldn't be absolutely necessary but as a good practice I would do that.
I would recommend using an int with a foreign key to a lookup table (like you describe in your second scenario). This will cause the index to be much smaller than indexing a VARCHAR so the storage required would be smaller. It should perform better, too.
Avitus is right, that it's generally a good practice to create lookups.
Think about the JOINS you will use this table in. 1000-20000 rows are not a lot to be handled by MySQL. If you don't have any, I would not bother about the lookups, just index the column.
BUT as soon as you start joining the table with others (of the same size) that's where the performance loss comes in, which you can (most likely) compensate by introducing lookups.
Based on the query I'm running now I assume this is a pipe dream:
I have an index on a column that contains a string id. Those IDs have an identifier on the end, so to capture the data I need I need to pattern match like so:
key LIKE '%racecar'
Since you can't take advantage of an index with the wildcard starting the string, I was hoping I could do this:
reverse(key) LIKE 'racecar%'
But, this would mean MySQL has to look at, and perform a function on, every single row anyway, is that correct? Any other ways to get around this issue short of changing the naming conventions?
This smells like bad DB design. Split the string and the id into two separate columns and the problem(and many other problems) will be automatically solved.
I also doubt the order of the string and the id will make difference to MYSQL with respect to performance.
Also keep in mind you have an index over key, but this does not mean you have an index over the reverse of key which is the reason you get no speedup at all. I believe that given the situation the performance is beyond salvation.
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.