It seems a same thing from users aspect.
A key-value database does not care about the contents or format of the value. It just allows you to store stuff under keys, and get it back again, and iterate keys.
Object Storage or Document Databases can look at the contents of the data you store in them, and allow you to query or index on something other than the key.
The would be one distinction to draw. But googling around for Object Storage, it seems that this is a rather ill-defined buzzword.
Related
What do developers commonly use as the key and value to cache the result from a SQL query into Redis? For example, if I have a Users table, and I want to cache the results from the query:
SELECT name, age FROM Users
1) Which Redis data structure should I use? Should I just have a single Key for the query and store the entire object returned by the database as the Value as such:
{ key: { object returned by database } }
Or should I use Redis' List data structure and loop through the rows individually and push them into the List as such:
{ key: [ ... ]}
Wouldn't this add computation time of O(N)? How is this more effective than just simply storing the object returned by the database?
Or should I use Redis' Hash Map data structure and loop through the rows individually and set a unique Key for each row with its corresponding attributes as such:
{ key1: {name: 'Bob', age: 25} }, { key2: {name: 'Sally', age: 15} }, ...
2) What would be a good rule of thumb with regards to the Key? From my understanding, some people just use the SQL query as the Key? But if you do so, does that mean you would have to store the entire object returned by the database as the Value (as per question 1)? Is this the best way to do it? If you are using an ORM, do you still use the SQL query as the key?
This is nicely analyzed in the Database Caching Strategies Using Redis whitepaper, by AWS.
Here the options discussed in the document. What is best is really a design decision based on tradeoffs you have to make for your specific use-case.
Cache the Database SQL ResultSet
Cache a serialized ResultSet object that contains the fetched database
row.
Pro: When data retrieval logic is abstracted (e.g., as in a Data Access Object or DAO layer), the consuming code expects only a
ResultSet object and does not need to be made aware of its
origination. A ResultSet object can be iterated over, regardless of
whether it originated from the database or was deserialized from the
cache, which greatly reduces integration logic. This pattern can be
applied to any relational database.
Con: Data retrieval still requires extracting values from the ResultSet object cursor and does not further simplify data access; it
only reduces data retrieval latency.
Cache Select Fields and Values in a Custom Format
Cache a subset of a fetched database row into a custom structure that
can be consumed by your applications.
Pro: This approach is easy to implement. You essentially store specific retrieved fields and values into a structure such as JSON or
XML and then SET that structure into a Redis string. The format you
choose should be something that conforms to your application’s data
access pattern.
Con: Your application is using different types of objects when querying for particular data (e.g., Redis string and database
results). In addition, you are required to parse through the entire
structure to retrieve the individual attributes associated with it.
Cache Select Fields and Values into an Aggregate Redis Data Structure
Cache the fetched database row into a specific data structure that can
simplify the application’s data access.
Pro: When converting the ResultSet object into a format that simplifies access, such as a Redis Hash, your application is able to
use that data more effectively. This technique simplifies your data
access pattern by reducing the need to iterate over a ResultSet object
or by parsing a structure like a JSON object stored in a string. In
addition, working with aggregate data structures, such as Redis Lists,
Sets, and Hashes provide various attribute level commands associated
with setting and getting data, eliminating the overhead associated
with processing the data before being able to leverage it.
Con: Your application is using different types of objects when querying for particular data (e.g., Redis Hash and database results).
Cache Serialized Application Object Entities
Cache a subset of a fetched database row into a custom structure that
can be consumed by your applications.
Pro: Use application objects in their native application state with simple serializing and deserializing techniques. This can
rapidly accelerate application performance by minimizing data
transformation logic.
Con: Advanced application development use case
Regarding 2)
What would be a good rule of thumb with regards to the Key?
Using the SQL query as the Key is OK for as long as you are sure it is unique. Add prefixes if there is a risk of not-uniqueness. You may have other databases with the same table names, leading to the same queries. Also make them invariant: all lower case or upper case. Redis keys are case-sensitive.
But if you do so, does that mean you would have to store the entire object returned by the database as the Value (as per question 1)?
Not necessarily, it comes down to what processing you are doing with the query. Chances are some are best stored as raw entire object for processing, some as JSON-stringified object to return quickly to the client, some as rows, etc. The best is to adapt accordingly.
Is this the best way to do it?
Not necessarily.
If you are using an ORM, do you still use the SQL query as the key?
You may if your ORM easily exposes the SQL Query programmatically, and it is consistent.
I wouldn't get fixed on the idea of using the SQL Query as key, use something you can be sure it is consistent, it will optimize your processing, and you'll have clear rules to invalidate. It could be the method call with parameters, the web API call, etc.
Is there an advantage to storing the metadata (or indexing data) for a document/*LOB separate from the raw data.
For instance having a table/collection/bucket with index on (name,school)
ID: 123
name: Johny
School: Harvard
Transcript: /*2MB text/binary*/
vs
Metadata
ID: 123
name: Johny
School: Harvard
Data
ID: 123
Transcripts: /*2MB text/binary*/
Let's assume mongodb, although it's really db agnostic perhaps.
db.firstModel.find({},{transcripts:0}) vs db.secondModel.find()
Additionally if we have aggregation/grouping on the metadata, would the heavy payload in transcripts weigh it down (even though the aggregation is on other fields)? is it better to aggregate on the metadata collection separately, then retrieve by id from the data collection? Or is it better to respect the database design (keeping everything coupled in a single document)?
In Couchbase, if it works for your use case, an option might be to have the object ID for your 2MB document something like harvard::johny::123. Every object would have such a pattern for each object ID that is used consistently in your application. Therefore your application easily piece together the object ID. Then you do not have to query or use views. You know it is harvard and johny and his 123rd object, you can just get it by ID. You already know the answer, no querying and so Couchbase will be very fast.
That being said, there may be other meta data that you want to keep in that metadata object and you want to index on and then yes, in Couchbase it might be better to break out the documents like you suggest. In Couchbase it might even be better to put them in separate buckets so the indexers are only looking at things it will index.
For an example that may not be entirely applicable to your use case, but should give you an idea of what is possible go here
All of that being said, from experience I do not like keeping larger object like you suggest in a DB long term, regardless of the DB. From an operational perspective it is terrible. You are storing what amounts to static data in a layer that needs to be very performant, with usually expensive storage and having to backup those objects over time. They become a boat anchor around your neck after a few months/years. I suggest keeping the meta-data in a fast performing system like Couchbase (cache+persistence with replication, etc) that also has a pointer to the large objects in something that is best for dishing out large static objects like HDFS, Amazon S3, etc.
We need to keep some in-memory data structure to keep english word dictionary in memory.
When the computer/wordweb starts,we need to read dictionary from disk into an in-memory data structure.
This question asks how do we populate from disk to in-memory data structure in typical real world dictionaries say wordweb?
Ideally we would like to keep dictionary in disk in the way, we require it in in-memory, so that we don't have to spend time building in-memory data structure, we just read it off the disk. But for linked lists, pointers etc, how do we store the same image in disk. Some relative addresses etc would help here?
Typically, is the entire dictionary read and stored in memory. or only part/handlers and leaf page IOs are done, when searching for a specific word.
If somebody wants to help with what that in-memory data structure is typically, please go ahead.
Thanks,
You mentioned pointers, so I'm assuming you're using C++; if that's the case and you want to read directly from disk into memory without having to "rebuild" your data structure, then you might want to look into serialization: How do you serialize an object in C++?
However, you generally don't want to load the entire dictionary anyway, especially if it's a user application. If the user is looking up dictionary words, then reading from disk happens so fast that the user will never notice the "delay." If you're servicing hundreds or thousands of requests, then it might make sense to cache the dictionary into memory.
So how many users do you have?
What kind of load are you expecting to have on the application?
Wordweb is using Sqlite Database at backend. It makes sense to me to use a Database system to store the content so its easier to GET the content which the user is looking for quickly.
Wordweb has Word prediction as well... so it will be a query to database like
select word from table where word='ab%';
on the other hand, when the user presses enter for the word
select meaning from table where word='abandon'
You do not want to be Serializing the content from disk to memory while the user is typing or after he has pressed Enter to search. Since the data will be large (Dictionary), Serialization will probably take time more then the user will tolerate for every word search.
Else why don't you create a JSON format File containing all the meaning creating a short form of Dictionary ?
I have an otherwise perfectly relational data schema in place for my Postgres 8.4 DB, but I need the ability to associate arbitrary key/value pairs with several of my tables, with the assigned keys varying by row. Key/value pairs are user-generated, so I have no way of predicting them ahead of time or wrangling orderly schema changes.
I have the following requirements:
Key/value pairs will be read often, written occasionally. Reads must be reasonably fast.
No (present) need to query off of the keys or values. (But it might come in handy some day.)
I see the following possible solutions:
The Entity-Attribute-Value pattern/antipattern. Annoying, but the annoyance would be generally offset by my ORM.
Storing key/value pairs as serialized JSON data on a text column. A simple solution, and again the ORM comes in handy, but I can kiss my future self's need for queries good-bye.
Storing key/value pairs in some other NoSQL db--probably a key/value or document store. ORM is no help here. I'll have to manage the separate queries (and looming data integrity issues?) myself.
I'm concerned about query performance, as I hope to have a lot of these some day. I'm also concerned about programmer performance, as I have to build, maintain, and use the darned thing. Is there an obvious best approach here? Or something I've missed?
That's precisely what the hstore datatype is for in PostgreSQL.
http://www.postgresql.org/docs/current/static/hstore.html
It's really fast (you can index it) and quite easy to handle. The only drawback is that you can only store character data, but you'd have that problem with the other solutions as well.
Indexes support "exists" operator, so you can query quite quickly for rows where a certain key is present, or for rows where a specific attribute has a specific value.
And with 9.0 it got even better because some size restrictions were lifted.
hstore is generally good solution for that, but personally I prefer to use plain key:value tables. One table with definitions, other table with values and relation to bind values to definition, and relation to bind values to particular record in other table.
Why I'm against hstore? Because it's like a registry pattern. Often mentioned as example of anti pattern. You can put anything there, it's hard to easy validate if it's still needed, when loading a whole row (in ORM especially), the whole hstore is loaded which can have much junk and very little sense. Not mentioning that there is need to convert hstore data type into your language type and convert back again when saved. So you get some overhead of type conversion.
So actually I'm trying to convert all hstores in company I'm working for into simple key:value tables. It's not that hard task though, because structures kept here in hstore are huge (or at least big), and reading/writing an object crates huge overhead of function calls. Thus making a simple task like that "select * from base_product where id = 1;" is making a server sweat and hits performance badly. Want to point that performance issue is not because db, but because python has to convert several times results received from postgres. While key:value is not requiring such conversion.
As you do not control data then do not try to overcomplicate this.
create table sometable_attributes (
sometable_id int not null references sometable(sometable_id),
attribute_key varchar(50) not null check (length(attribute_key>0)),
attribute_value varchar(5000) not null,
primary_key(sometable_id, attribute_key)
);
This is like EAV, but without attribute_keys table, which has no added value if you do not control what will be there.
For speed you should periodically do "cluster sometable_attributes using sometable_attributes_idx", so all attributes for one row will be physically close.
I have a HashMap that I am serializing and deserializing to an Oracle db, in a BLOB data type field.
I want to perform a query, using this field.
Example, the application will make a new HashMap, and have some key-value pairs.
I want to query the db to see if a HashMap with this data already exists in the db.
I do not know how to do this, it seems strange if i have to go to every record in the db, deserialize it, then compare, Does SQL handle comparing BLOBs, so i could have...select * from PROCESSES where foo = ?....and foo is a BLOB type, and the ? is an instance of the new HashMap?
Thanks
Here's an article for you to read: Pounding a Nail: Old Shoe or Glass Bottle
I haven't heard much about your application's underlying architecture, but I can tell you immediately that there is never a reason why you should need to use a HashMap in this way. Its a bad technique, plain and simple.
The answer to your question is not a clever Oracle query, its a redesign of your application's architecture.
For a start, you should not serialize a HashMap to a database (more generally, you shouldn't serialize anything that you need to query against). Its much easier to create a table to represent hashmaps in your application as follows:
HashMaps
--------
MapID (pk int)
Key (pk varchar)
Value
Once you have the content of your hashmaps in your database, its trivial to query the database to see if the data already exists or produce any other kind of aggregate data:
SELECT Count(*) FROM HashMaps where MapID = ? AND Key = ?
Storing serialized objects in a database is almost always a bad idea, unless you know ahead of time that you don't need to query against them.
How are you serializing the HashMap? There are lots of ways to serialize data and an object like a HashMap. Comparing two maps, especially in serialized form, is not trivial, unless your serialization technique guarantees that two equivalent maps always serialize the same way.
One way you can get around this mess is to use XML serialization for some objects that rarely need to be queried. For example, where I work we have a log table where a certain log message is stored as an XML file in a CLOB field. This xml data represents a serialized Java object. Normally we query against other columns in the record, and only read/write the blob in single atomic steps. However once or twice it was necessary to do some deep inspection of the blob, and using XML allowed this to happen (Oracle supports querying XML in varchar2 or CLOB fields as well as native XML objects). It's a useful technique if used sparingly.
Look into dbms_crypto.hash to make a hash of your blob. Store the hash alongside the blob and it will give you something to narrow down the search to something manageable. I'm not recommending storing the hash map, but this is a general technique for searching for an exact match between blobs.
See also SQL - How do you compare a CLOB
i cannot disagree, but i'm being told to do so.
i appreciate your solution, and that's sort of what i had previously.
thanks
I haven't had the need to compare BLOBs, but it appears that it's supported through the dbms_lob package.
See dbms_lob.compare() at http://www.psoug.org/reference/dbms_lob.html
Oracle can have new data types defined with Java (or .net on windows) you could define a data type for your serialized object and define how queries work on it.
Good lack if you try this...
If you serialize your data to xml, and store the data in an xml you can then use xpaths within your sql query. (Sorry as I am more of a SqlServer person, I don’t know the details of how to do this in Oracle.)
If you EVERY need to update only part of the serialized data don’t do this.
Likewise if any of the data is pointed to by other data or points to other data don’t do this.