I was exposed to the world of tables and data structures in R before the RDBMS systems and other database systems. It is quite elegant in R/Python to create tables and lists from stuctured data (.csv or other formats) and then do data manipulations programmatically.
Last year, I attended a course in Database management and learnt all about structured and unstructured databases. I also noticed that it is the norm to feed data from multiple sources of data into databases rather than directly use them in R (for convenience and discipline?).
For research purposes, R seems to suffice, for joining, appending or even complicated data manipulations.
The questions that keeps arising is:
When to use R directly by using commands such as read.csv, when to use R by creating database and querying from tables using the R-SQL interface?
For instance, if I have a multi-source data, like (a) Person level information (age, gender, smoking habits), (b) Outcome variables (such as surveys taken by them in real time), (c) Covariate information (environment characteristics), (d) Treatment input (occurrence of an event that modifies the outcome - survey response) (d) Time and space information of participants taking survey
How to approach the data collection and processing in this case. There may be standard industry procedures, but I put this question forward here, to understand list of feasible and optimal approaches that individuals and small group of researchers can adopt.
What you're describing when you say "that it is the norm to feed data from multiple sources of data into databases" sounds more specifically like a data warehouse. Databases are used for many reasons, and in plenty of situations they will hold data from one source - for instance, a database used as the data store of a transactional system will often only hold the data needed to run that system, and the data produced by that system.
The process you're describing is commonly called Extract, Transform, Load (ETL), and you might find looking up information about ETL and data warehousing helpful if you decide to go in the direction of combining your data prior to working with it in R.
I can't tell you which you should choose, or the optimal way of accomplishing it, because it will vary in different situations and might even come down to opinion. What I can tell you are some of the reasons why people create data warehouses, and you can decide for yourself whether it might be useful in your situation:
A data warehouse can provide a central location to hold combined data. This means that people do not need to combine the data themselves each time they need to use that specific combination of data. Unlike something like a simple one-off report or extract of combined data, it should provide some flexibility, letting people obtain the combined set of data they need for a specific task. Very often, in enterprise situations, multiple things are then be run on top of the same combined set of data - multidimensional data analysis tools (cubes), reports, data mining, etc.
Some of the benefits of this might include:
Individuals saving time when they otherwise would have needed to combine the data themselves.
If the data which needs to be combined is complex, or some people do not have proficiency at handling that part of the process, then there is less risk of data being combined incorrectly; you can be sure that different pieces of work have used the same source data.
If the data suffers from data quality issues, you resolve this once in the data warehouse, rather than working around it or resolving it repeatedly in code.
If new data is constantly being received, collection and integration of this into the data warehouse can be carried out automatically.
Like I say, I can't decide for you whether this is a useful direction or not - as with any decision of this kind you'll need to weigh up the costs of implementing such a solution against the benefits, and both will be specific to your individual case. But hopefully this answers your core question of why someone might choose to do this work in a database instead of in their code, and gives you a starting point to work from.
Many times I read that data in NOSQL databases is stored denormalized. For instance consider a chess game record. It may not only contain the player id's that participate in the chess game, but also the first and lastname of that player. I suppose this is done because joins are not possible in NOSQL, so if you just duplicate data you can still retrieve all the data you want in one call without manual application level processing of the data.
What I don't understand is that now when you want to update a chess-player's name, you will have to write a query that updates both the chess-game records in which that player participates as well as the player record of that player. This seems like a huge performance overhead as the database will have to search all games where that player participates in and then update each of those records.
Is it true that data is often stored denormalized like in my example?
You are correct, the data is often stored de-normalized in NoSQL databases.
The problem with the updates is partially where the term "eventual consistency" comes from.
In your example, when you update the player's name (not a common event, but it can happen), you would issue a background job to update the name across all other records. Yes, while the update is happening you may retrieve an older value, but eventually the data will be consistent. Since we're not writing ATM software here, the performance/consistency tradeoff is acceptable.
You can find more info here: http://www.allbuttonspressed.com/blog/django/2010/09/JOINs-via-denormalization-for-NoSQL-coders-Part-2-Materialized-views
One way to look at it is that the number of times the user changes his/her name is extremely rare.
But the number of times that board data is read and changed is immense.
So it only makes sense to optimize for a case that will happen so much more times than a case that's only happening ever so rarely.
Another point to note is that by not keeping that name data duplicated under board data, you are actually increasing the performance overhead of the read. Every time you fetch the board data, you'd have to go one more step ahead and fetch all the user data too (even if all you really wanted was just first and last name).
Again the reason to put that first name and last name on board data is probably that on the screen where the board data will be shown, you'll often be showing the user's name too.
For these reasons, you are spared to have duplicate data on NoSQL DBs. (Although this can be done in SQL DBs too but mind ya, you'll be frowned upon). Duplication in NoSQL world is fairly common and is promoted too.
I have been working for the past 7 years with NoSQL (Firestore) for 2 fairly big projects where I was able to write code from scratch (both around 50k LoC and one has about 15k daily active users). I didn't use denormalization at all. The concept never appealed to me, and document reads are fairly cheap in Firestore.
To come back to your example; loading the other data for the chess game seems way more important than instantly being able to show the name. I would load the name based on the user id in the background and put a simple client-side memoize / cache around it to prevent fetching the same user document over and over.
What I did use quite a bit to solve performance issues is generate derived data. I would set a listener on a database document "onWrite" and then store some computed data in another derived document. These documents would automatically update when the source changes, so it doesn't complicate things really. In the case of a chess game, a distilled document could be the leaderboard that is constantly shown to all users of the app.
Another optimization I had to do was to distill a long list of titles + metadata for recently opened "projects". Firestore on the web client side doesn't give the ability to select fields from a document in a query. It only fetches full documents and that was too much data for the list, so we solved this by making an API endpoint to fetch the distilled data through there.
I'm not saying you should follow my advice, but we seem to be doing well in terms of code complexity and database costs. So when I read that NoSQL requires data denormalization I become skeptical :)
That's my 2 cents.
I am not sure if there is a term to describe this, but I have observed that content management systems store all kinds of data in a single table with their bare minimum properties while the meta data is stored in another table in form of key value pairs.
for eg. everything (blog posts, pages, images, events etc) is stored in one table and considered as a post.
I understand that this allows for abstraction and easy extensibility
we are considering designing our new project this way. It is not exactly a CMS but we plan to keep adding modules to it in stages. Lets say initially there will be only posts and images on which comments can be posted. Later on we might add videos which will also have the commenting feature.
what are the drawbacks of this approach ? and will it work for a requirement like ours ?
Thanks
The drawback is that the main table will get zillions of reads (and plenty of writes, too).
This means that there will be lots of lock contentions, heavy reindexing etc.
In order to mitigate this a bit you may consider splitting the "main table" in a series of not-so-main-tables.
Say, you will have one main table for "Posts" (possibly refined through metadata or subtables for specific types of posts, like Sticky, Announcement, Shoutbox, Private...)
One main table for Images (possibly refined for gifs, jpegs etc.)
One main table for Videos...
If this is a custom application (and not intended to be something that has to be "infinitely tweakable" like a CMS or a Portal framework) I think this kind of split is acceptable, and may provide some better performance (if you expect to have large amounts of data).
Regarding your "examples" comment... first of all, if you keep comments again in a single gigantic table you may have similar problems as if you kept all type of items in it.
Assuming this is not a problem, you can obviously put a sort of reference key (you can't use the normal foreign keys, of course) that links comments to their original item.
This works fine when you go from item to comments, a bit less when you have to move from comments to the originating item. So the tradeoff is about what kind of operations would be more frequent for your problem.
Simplicity and extensibility are indeed often attractive aspects of attribute-value and (as you say) "single table of things" approaches.
There's no 100% right answer here -- depending on your performance/throughput goals and extensibility needs, this approach might work for you too.
In most cases, however, where you know what kinds of data you will store, it's usually in your interest to model distinct entities into their own tables and relate the data accordingly. RDBMSes have been architected and refined over decades to cater to this use case and to simply use tables as generic dumping grounds doesn't typically buy you any distinct advantages, except the act of delaying the inevitable need to model your data properly. Furthermore, when you boil everything into one table, you then force users outside your app itself (if you have any, for example report writers) to have to struggle with your "model within a model", which can just make folks frustrated when they write queries, etc. And you will sink to your lowest common denominator -- if you want to optimize queries about type X and you have types Y and Z in that same table in droves, they will impact performance on querying X.
Again, to be clear, there is distinct benefit to the "all things in one table" name/value style metadata approaches. I have used them myself and turned against modeling for similar reasons. However, my advice is to limit yourself to times when you really need to do that (i.e., you need to implement something before you can correctly model the space of things you will need). Most typically, I find myself doing that when I'm prototyping complex systems and I need to get something going sooner than later.
Original Question:
Hello,
I am creating very simple hobby project - browser based multiplayer game. I am stuck at designing tables for storing information about quest / skill requirements.
For now, I designed my tables in following way:
table user (basic information about users)
table stat (variety of stats)
table user_stats (connecting each user with stats)
Another example:
table monsters (basic information about npc enemies)
table monster_stats (connecting monsters with stats, using the same stat table from above)
Those were the simple cases. I must admit, that I am stuck while designing requirements for different things, e.g quests. Sample quest A might have only minimum character level requirement (and that is easy to implement) - but another one, quest B has multitude of other reqs (finished quests, gained skills, possessing specific items, etc) - what is a good way of designing tables for storing this kind of information?
In a similar manner - what is an efficient way of storing information about skill requirements? (specific character class, min level, etc).
I would be grateful for any help or information about creating database driven games.
Edit:
Thank You for the answers, yet I would like to receive more. As I am having some problems designing an rather complicated database layout for craftable items, I am starting a max bounty for this question.
I would like to receive links to articles / code snippets / anything connected with best practices of designing databases for storing game data (an good example of this kind of information is availibe on buildingbrowsergames.com).
I would be grateful for any help.
I'll edit this to add as many other pertinent issues as I can, although I wish the OP would address my comment above. I speak from several years as a professional online game developer and many more years as a hobbyist online game developer, for what it's worth.
Online games imply some sort of persistence, which means that you have broadly two types of data - one is designed by you, the other is created by the players in the course of play. Most likely you are going to store both in your database. Make sure you have different tables for these and cross-reference them properly via the usual database normalisation rules. (eg. If your player crafts a broadsword, you don't create an entire new row with all the properties of a sword. You create a new row in the player_items table with the per-instance properties, and refer to the broadsword row in the item_types table which holds the per-itemtype properties.) If you find a row of data is holding some things that you designed and some things that the player is changing during play, you need to normalise it out into two tables.
This is really the typical class/instance separation issue, and applies to many things in such games: a goblin instance doesn't need to store all the details of what it means to be a goblin (eg. green skin), only things pertinent to that instance (eg. location, current health). Some times there is a subtlety to the act of construction, in that instance data needs to be created based on class data. (Eg. setting a goblin instance's starting health based upon a goblin type's max health.) My advice is to hard-code these into your code that creates the instances and inserts the row for it. This information only changes rarely since there are few such values in practice. (Initial scores of depletable resources like health, stamina, mana... that's about it.)
Try and find a consistent terminology to separate instance data from type data - this will make life easier later when you're patching a live game and trying not to trash the hard work of your players by editing the wrong tables. This also makes caching a lot easier - you can typically cache your class/type data with impunity because it only ever changes when you, the designer, pushes new data up there. You can run it through memcached, or consider loading it all at start up time if your game has a continuous process (ie. is not PHP/ASP/CGI/etc), etc.
Remember that deleting anything from your design-side data is risky once you go live, since player-generated data may refer back to it. Test everything thoroughly locally before deploying to the live server because once it's up there, it's hard to take it down. Consider ways to be able to mark rows of such data as removed in a safe fashion - maybe a boolean 'live' column which, if set to false, means it just won't show up in the typical query. Think about the impact on players if you disable items they earned (and doubly if these are items they paid for).
The actual crafting side can't really be answered without knowing how you want to design your game. The database design must follow the game design. But I'll run through a trivial idea. Maybe you will want to be able to create a basic object and then augment it with runes or crystals or whatever. For that, you just need a one-to-many relationship between item instance and augmentation instance. (Remember, you might have item type and augmentation type tables too.) Each augmentation can specify a property of an item (eg. durability, max damage done in combat, weight) and a modifier (typically as a multiplier, eg. 1.1 to add a 10% bonus). You can see my explanation for how to implement these modifying effects here and here - the same principles apply for temporary skill and spell effects as apply for permanent item modification.
For character stats in a database driven game, I would generally advise to stick with the naïve approach of one column (integer or float) per statistic. Adding columns later is not a difficult operation and since you're going to be reading these values a lot, you might not want to be performing joins on them all the time. However, if you really do need the flexibility, then your method is fine. This strongly resembles the skill level table I suggest below: lots of game data can be modelled in this way - map a class or instance of one thing to a class or instance of other things, often with some additional data to describe the mapping (in this case, the value of the statistic).
Once you have these basic joins set up - and indeed any other complex queries that result from the separation of class/instance data in a way that may not be convenient for your code - consider creating a view or a stored procedure to perform them behind the scenes so that your application code doesn't have to worry about it any more.
Other good database practices apply, of course - use transactions when you need to ensure multiple actions happen atomically (eg. trading), put indices on the fields you search most often, use VACUUM/OPTIMIZE TABLE/whatever during quiet periods to keep performance up, etc.
(Original answer below this point.)
To be honest I wouldn't store the quest requirement information in the relational database, but in some sort of script. Ultimately your idea of a 'requirement' takes on several varying forms which could draw on different sorts of data (eg. level, class, prior quests completed, item possession) and operators (a level might be a minimum or a maximum, some quests may require an item whereas others may require its absence, etc) not to mention a combination of conjunctions and disjunctions (some quests require all requirements to be met, whereas others may only require 1 of several to be met). This sort of thing is much more easily specified in an imperative language. That's not to say you don't have a quest table in the DB, just that you don't try and encode the sometimes arbitrary requirements into the schema. I'd have a requirement_script_id column to reference an external script. I suppose you could put the actual script into the DB as a text field if it suits, too.
Skill requirements are suited to the DB though, and quite trivial given the typical game system of learning skills as you progress through levels in a certain class:
table skill_levels
{
int skill_id FOREIGN KEY;
int class_id FOREIGN KEY;
int min_level;
}
myPotentialSkillList = SELECT * FROM skill_levels INNER JOIN
skill ON skill_levels.skill_id = skill.id
WHERE class_id = my_skill
ORDER BY skill_levels.min_level ASC;
Need a skill tree? Add a column prerequisite_skill_id. And so on.
Update:
Judging by the comments, it looks like a lot of people have a problem with XML. I know it's cool to bash it now and it does have its problems, but in this case I think it works. One of the other reasons that I chose it is that there are a ton of libraries for parsing it, so that can make life easier.
The other key concept is that the information is really non-relational. So yes, you could store the data in any particular example in a bunch of different tables with lots of joins, but that's a pain. But if I kept giving you a slightly different examples I bet you'd have to modify your design ad infinitum. I don't think adding tables and modifying complicated SQL statements is very much fun. So it's a little frustrating that #scheibk's comment has been voted up.
Original Post:
I think the problem you might have with storing quest information in the database is that it isn't really relational (that is, it doesn't really fit easily into a table). That might be why you're having trouble designing tables for the data.
On the other hand, if you put your quest information directly into code, that means you'll have to edit the code and recompile each time you want to add a quest. Lame.
So if I was you I might consider storing my quest information in an XML file or something similar. I know that's the generic solution for just about anything, but in this case it sounds right to me. XML is really made for storing non-relation and/or hierarchical data, just like the stuff you need to store for your quest.
Summary: You could come up with your own schema, create your XML file, and then load it at run time somehow (or even store the XML in the database).
Example XML:
<quests>
<quest name="Return Ring to Mordor">
<characterReqs>
<level>60</level>
<finishedQuests>
<quest name="Get Double Cheeseburger" />
<quest name="Go to Vegas for the Weekend" />
</finishedQuests>
<skills>
<skill name="nunchuks" />
<skill name="plundering" />
</skills>
<items>
<item name="genie's lamp" />
<item name="noise cancelling headphones for robin williams' voice />
</items>
</characterReqs>
<steps>
<step number="1">Get to Mordor</step>
<step number="2">Throw Ring into Lava</step>
<step number="3">...</step>
<step number="4">Profit</step>
</steps>
</quest>
</quests>
It sounds like you're ready for general object oriented design (OOD) principles. I'm going to purposefully ignore the context (gaming, MMO, etc) because that really doesn't matter to how you do a design process. And me giving you links is less useful than explaining what terms will be most helpful to look up yourself, IMO; I'll put those in bold.
In OOD, the database schema comes directly from your system design, not the other way around. Your design will tell you what your base object classes are and which properties can live in the same table (the ones in 1:1 relationship with the object) versus which to make mapping tables for (anything with 1:n or n:m relationships - for exmaple, one user has multiple stats, so it's 1:n). In fact, if you do the OOD correctly, you will have zero decisions to make regarding the final DB layout.
The "correct" way to do any OO mapping is learned as a multi-step process called "Database Normalization". The basics of which is just as I described: find the "arity" of the object relationships (1:1, 1:n,...) and make mapping tables for the 1:n's and n:m's. For 1:n's you end up with two tables, the "base" table and a "base_subobjects" table (eg. your "users" and "user_stats" is a good example) with the "foreign key" (the Id of the base object) as a column in the subobject mapping table. For n:m's, you end up with three tables: "base", "subobjects", and "base_subobjects_map" where the map has one column for the base Id and one for the subobject Id. This might be necessary in your example for N quests that can each have M requirements (so the requirement conditions can be shared among quests).
That's 85% of what you need to know. The rest is how to handle inheritance, which I advise you to just skip unless you're masochistic. Now just go figure out how you want it to work before you start coding stuff up and the rest is cake.
The thread in #Shea Daniel's answer is on the right track: the specification for a quest is non-relational, and also includes logic as well as data.
Using XML or Lua are examples, but the more general idea is to develop your own Domain-Specific Language to encode quests. Here are a few articles about this concept, related to game design:
The Whimsy Of Domain-Specific Languages
Using a Domain Specific Language for Behaviors
Using Domain-Specific Modeling towards Computer Games Development Industrialization
You can store the block of code for a given quest into a TEXT field in your database, but you won't have much flexibility to use SQL to query specific parts of it. For instance, given the skills a character currently has, which quests are open to him? This won't be easy to query in SQL, if the quest prerequisites are encoded in your DSL in a TEXT field.
You can try to encode individual prerequisites in a relational manner, but it quickly gets out of hand. Relational and object-oriented just don't go well together. You can try to model it this way:
Chars <--- CharAttributes --> AllAttributes <-- QuestPrereqs --> Quests
And then do a LEFT JOIN looking for any quests for which no prereqs are missing in the character's attributes. Here's pseudo-code:
SELECT quest_id
FROM QuestPrereqs
JOIN AllAttributes
LEFT JOIN CharAttributes
GROUP BY quest_id
HAVING COUNT(AllAttributes) = COUNT(CharAttributes);
But the problem with this is that now you have to model every aspect of your character that could be a prerequisite (stats, skills, level, possessions, quests completed) as some kind of abstract "Attribute" that fits into this structure.
This solves this problem of tracking quest prerequisites, but it leaves you with another problem: the character is modeled in a non-relational way, essentially an Entity-Attribute-Value architecture which breaks a bunch of relational rules and makes other types of queries incredibly difficult.
Not directly related to the design of your database, but a similar question was asked a few weeks back about class diagram examples for an RPG
I'm sure you can find something useful in there :)
Regarding your basic structure, you may (depending on the nature of your game) want to consider driving toward convergence of representation between player character and non-player characters, so that code that would naturally operate the same on either doesn't have to worry about the distinction. This would suggest, instead of having user and monster tables, having a character table that represents everything PCs and NPCs have in common, and then a user table for information unique to PCs and/or user accounts. The user table would have a character_id foreign key, and you could tell a player character row by the fact that a user row exists corresponding to it.
For representing quests in a model like yours, the way I would do it would look like:
quest_model
===============
id
name ['Quest for the Holy Grail', 'You Killed My Father', etc.]
etc.
quest_model_req_type
===============
id
name ['Minimum Level', 'Skill', 'Equipment', etc.]
etc.
quest_model_req
===============
id
quest_id
quest_model_req_type_id
value [10 (for Minimum Level), 'Horseback Riding' (for Skill), etc.]
quest
===============
id
quest_model_id
user_id
status
etc.
So a quest_model is the core definition of the quest structure; each quest_model can have 0..n associated quest_model_req rows, which are requirements specific to that quest model. Every quest_model_req is associated with a quest_model_req_type, which defines the general type of requirement: achieving a Minimum Level, having a Skill, possessing a piece of Equipment, and so on. The quest_model_req also has a value, which configures the requirement for this specific quest; for example, a Minimum Level type requirement might have a value of 20, meaning you must be at least level 20.
The quest table, then, is individual instances of quests that players are undertaking or have undertaken. The quest is associated with a quest_model and a user (or perhaps character, if you ever want NPCs to be able to do quests!), and has a status indicating where the progress of the quest stands, and whatever other tracking turns out useful.
This is a bare-bones structure that would, of course, have to be built out to accomodate the needs of particular games, but it should illustrate the direction I'd recommend.
Oh, and since someone else threw around their credentials, mine are that I've been a hobbyist game developer on live, public-facing projects for 16 years now.
I'd be extremely careful of what you actually store in a DB, especially for an MMORPG. Keep in mind, these things are designed to be MASSIVE with thousands of users, and game code has to execute excessively quickly and send a crap-ton of data over the network, not only to the players on their home connections but also between servers on the back-end. You're also going to have to scale out eventually and databases and scaling out are not two things that I feel mix particularly well, particularly when you start sharding into different regions and then adding instance servers to your shards and so on. You end up with a whole lot of servers talking to databases and passing a lot of data, some of which isn't even relevant to the game at all (SQL text going to a SQL server is useless network traffic that you should cut down on).
Here's a suggestion: Limit your SQL database to storing only things that will change as players play the game. Monsters and monster stats will not change. Items and item stats will not change. Quest goals will not change. Don't store these things in a SQL database, instead store them in the code somewhere.
Doing this means that every server that ever lives will always know all of this information without ever having to query a database. Now, you don't store quests at all, you just store accomplishments of the player and the game programatically determines the affects of those quests being completed. You don't waste data transferring information between servers because you're only sending event ID's or something of that nature (you can optimize the data you pass by only using just enough bits to represent all the event ID's and this will cut down on network traffic. May seem insignificant but nothing is insignificant in massive network apps).
Do the same thing for monster stats and item stats. These things don't change during gameplay so there's no need to keep them in a DB at all and therefore this information NEVER needs to travel over the network. The only thing you store is the ID of the items or monster kills or anything like that which is non-deterministic (i.e. it can change during gameplay in a way which you can't predict). You can have dedicated item servers or monster stat servers or something like that and you can add those to your shards if you end up having huge numbers of these things that occupy too much memory, then just pass the data that's necessary for a particular quest or area to the instance server that is handling that thing to cut down further on space, but keep in mind that this will up the amount of data you need to pass down the network to spool up a new instance server so it's a trade-off. As long as you're aware of the consequences of this trade-off, you can use good judgement and decide what you want to do. Another possibility is to limit instance servers to a particular quest/region/event/whatever and only equip it with enough information to the thing it's responsible for, but this is more complex and potentially limits your scaling out since resource allocation will become static instead of dynamic (if you have 50 servers of each quest and suddenly everyone goes on the same quest, you'll have 49 idle servers and one really swamped server). Again, it's a trade-off so be sure you understand it and make good choices for your application.
Once you've identified exactly what information in your game is non-deterministic, then you can design a database around that information. That becomes a bit easier: players have stats, players have items, players have skills, players have accomplishments, etc, all fairly easy to map out. You don't need descriptions for things like skills, accomplishments, items, etc, or even their effects or names or anything since the server can determine all that stuff for you from the ID's of those things at runtime without needing a database query.
Now, a lot of this probably sounds like overkill to you. After all, a good database can do queries very rapidly. However, your bandwidth is extremely precious, even in the data center, so you need to limit your use of it to only what is absolutely necessary to send and only send that data when it's absolutely necessary that it be sent.
Now, for representing quests in code, I would consider the specification pattern (http://en.wikipedia.org/wiki/Specification_pattern). This will allow you to easily build up quest goals in terms of what events are needed to ensure that the specification for completing that quest is met. You can then use LUA (or something) to define your quests as you build the game so that you don't have to make massive code changes and rebuild the whole damn thing to make it so that you have to kill 11 monsters instead of 10 to get the Sword of 1000 truths in a particular quest. How to actually do something like that I think is beyond the scope of this answer and starts to hit the edge of my knowledge of game programming so maybe someone else on here can help you out if you choose to go that route.
Also, I know I used a lot of terms in this answer, please ask if there are any that you are unfamiliar with and I can explain them.
Edit: didn't notice your addition about craftable items. I'm going to assume that these are things that a player can create specifically in the game, like custom items. If a player can continually change these items, then you can just combine the attributes of what they're crafted as at runtime but you'll need to store the ID of each attribute in the DB somewhere. If you make a finite number of things you can add on (like gems in Diablo II) then you can eliminate a join by just adding that number of columns to the table. If there are a finite number of items that can be crafted and a finite number of ways that differnet things can be joined together into new items, then when certain items are combined, you needn't store the combined attributes; it just becomes a new item which has been defined at some point by you already. Then, they just have that item instead of its components. If you clarify the behavior your game is to have I can add additional suggestions if that would be useful.
I would approach this from an Object Oriented point of view, rather than a Data Centric point of view. It looks like you might have quite a lot of (poss complex) objects - I would recommend getting them modeled (with their relationships) first, and relying on an ORM for persistence.
When you have a data-centric problem, the database is your friend. What you have done so far seems to be quite right.
On the other hand, the other problems you mention seem to be behaviour-centric. In this case, an object-oriented analisys and solution will work better.
For example:
Create a quest class with specificQuest child classes. Each child should implement a bool HasRequirements(Player player) method.
Another option is some sort of rules engine (Drools, for example if you are using Java).
If i was designing a database for such a situation, i might do something like this:
Quest
[quest properties like name and description]
reqItemsID
reqSkillsID
reqPlayerTypesID
RequiredItems
ID
item
RequiredSkills
ID
skill
RequiredPlayerTypes
ID
type
In this, the ID's map to the respective tables then you retrieve all entries under that ID to get the list of required items, skills, what have you. If you allow dynamic creation of items then you should have a mapping to another table that contains all possible items.
Another thing to keep in mind is normalization. There's a long article here but i've condensed the first three levels into the following more or less:
first normal form means that there are no database entries where a specific field has more than one item in it
second normal form means that if you have a composite primary key all other fields are fully dependent on the entire key not just parts of it in each table
third normal is where you have no non-key fields that are dependent on other non-key fields in any table
[Disclaimer: i have very little experience with SQL databases, and am new to this field. I just hope i'm of help.]
I've done something sort of similar and my general solution was to use a lot of meta data. I'm using the term loosely to mean that any time I needed new data to make a given decision(allow a quest, allow using an item etc.) I would create a new attribute. This was basically just a table with an arbitrary number of values and descriptions. Then each character would have a list of these types of attributes.
Ex: List of Kills, Level, Regions visited, etc.
The two things this does to your dev process are:
1) Every time there's an event in the game you need to have a big old switch block that checks all these attribute types to see if something needs updating
2) Everytime you need some data, check all your attribute tables BEFORE you add a new one.
I found this to be a good rapid development strategy for a game that grows organically(not completely planned out on paper ahead of time) - but it's one big limitation is that your past/current content(levels/events etc) will not be compatible with future attributes - i.e. that map won't give you a region badge because there were no region badges when you coded it. This of course requires you to update past content when new attributes are added to the system.
just some little points for your consideration :
1) Always Try to make your "get quest" requirements simple.. and "Finish quest" requirements complicated..
Part1 can be done by "trying to make your quests in a Hierarchical order":
example :
QuestA : (Kill Raven the demon) (quest req: Lvl1)
QuestA.1 : Save "unkown" in the forest to obtain some info.. (quest req : QuestA)
QuestA.2 : Craft the sword of Crystal ... etc.. (quest req : QuestA.1 == Done)
QuestA.3 : ... etc.. (quest req : QuestA.2 == Done)
QuestA.4 : ... etc.. (quest req : QuestA.3 == Done)
etc...
QuestB (Find the lost tomb) (quest req : ( QuestA.statues == Done) )
QuestC (Go To the demons Hypermarket) ( Quest req: ( QuestA.statues == Done && player.level== 10)
etc....
Doing this would save you lots of data fields/table joints.
ADDITIONAL THOUGHTS:
if you use the above system, u can add an extra Reward field to ur quest table called "enableQuests" and add the name of the quests that needs to be enabled..
Logically.. you'd have an "enabled" field assigned to each quest..
2) A minor solution for Your crafting problem, create crafting recipes, Items that contains To-be-Crafted-item crafting requirements stored in them..
so when a player tries to craft an item.. he needs to buy a recipe 1st.. then try crafting..
a simple example of such item Desc would be:
ItemName: "Legendary Sword of the dead"
Craftevel req. : 75
Items required:
Item_1 : Blade of the dead
Item_2 : A cursed seal
item_3 : Holy Gemstone of the dead
etc...
and when he presses the "craft" Action, you can parse it and compare against his inventory/craft box...
so Your Crafting DB will have only 1 field (or 2 if u want to add a crafting LvL req. , though it will already be included in the recipe.
ADDITIONAL THOUGHTS:
Such items, can be stored in xml format in the table .. which would make it much easier to parse...
3) A similar XML System can be applied to Your quest system.. to implement quest-ending requirements..
Recently I’ve found myself in a database tangle where management wants the ability to remove data from the database, but still wants that data to appear in other places. Example: They want to remove all instances of the product whizbang, but they still want whizbang to appear in sales reports. (if they ran one for a previous date).
Now I can add a field, say is_deleted, that will track whether that product has been deleted and thus still keep all my references, but over a period of time, I have the potential of housing a lot of dead data. (data that is never accessed again). How to handle this is not my question.
I’m curious to find out, in your experience what is the average life span of data? That is, on average how long is data alive or good for before it gets either replaced or deleted? I understand that this is relative to the type of data you are housing, but certainly all data has some sort of life span?
Data lives forever...or often it should. One common practice is to have end and/or start dates for a record. So for your whizbang, you have a start date (so that it won't appear on sales reports before it's official launch), and an end date (so that it drops off of reports after it's been end-of-lifed). Using the proper dates as criteria for your reporting as well as your applications, you won't see the whizbang except for when you should, and the data still exists (which it should, theoretically infinitely).
As Koistya Navin mentions, moving data to a data warehouse at a certain point is also an option, but this depends in large part on how large your 'old' data is, and how long you need to keep it readily available for access.
Many of our customers keep data online for 2 years. After that it's moved to backup disks, but it can be put online if needed.
Consider adding a column "expiration" or "effective date". This will allow you mark a product as obsolete, but reports will return that product if the time range is satisfied.
Usually it's better to move such data into seporate database (database warehouse) and keep working database clean. At data warehouse your data can be kept for many years without impacting your application.
Reference: Data Warehouse at Wikipedia
I've always gone by what is the ruling body looking for. Example the IRS wants you to keep 7 years of history or for security reasons we keep 3 years of log information, etc. So I guess you could do 2 things, determine what the life span of your data is I would say 3 years would be enough and then you could add the is_deleted flag along with a date that way you would be able to flag some data to delete sooner than later.
Yes, all data has a lifespan. And yes, it is relative to the type of data you have.
Some data has a lifespan measured in seconds (authentication tokens, for instance), some other data virtual eternity (more than the medium and formats it is stored into, like for instance ownership records).
You will have to either be more specific as to the type of data you are envisioning, or do a census in your own organization as to the usual lifespan of stuff.
Our particular flavor varies. We have some data (a vast majority) which goes stale after 3 months (hard product limit) but can be revived at any later date.
We have other data that is effectively immortal.
In practice, most of the data we serve up is fresh and frequently requested for a few weeks, at most a month, before falling to sporadic use.
How much is "a lot of dead data"?
With processing power and data storage so cheap, I wouldn't purge old data unless there's a really good reason to. You also need to consider the legal implications. Large (and even small) companies may have incredibly long retention policies for old data, to save themselves millions down the road when they are subpoenaed for it by a judge.
I would check with whatever legal department you have and find out how long the data needs to be stored. That's the safest bet.
Also, ask yourself what the benefit of removing the old data is. Is the only benefit a tidier database? If so, I wouldn't do it. Are you going to see a 10X performance increase? If so, I'd do it. This really is a complex question though, and it's tough for us to have all the information required to give you good advice.
I have a few projects where the customer wants all the historical data (going back over 19 years). Quite a bit of the really old data is malformed and is going to be a nightmare to import into the new system. We convinced them that they won't need records going back any further than 10 years, but like you said it's all relative to the type of data you're housing.
On a side note, data storage is extremely cheap right now, and if it isn't affecting the performance of your application, I would just leave it where it is.
[...] but certainly all data has some sort of life span?
Not any kind of life span we can talk about meaningfully. A lot of data is useless as soon as it's created or recorded. Such data could be discarded immediately with no effect. On the other hand, some data has enough value that it will outlive the current system that hosts it. If Amazon were to completely replace their current infrastructure, the customer histories they have stored would still be immensely valuable.
As you said, it's relative. Each type of data has its own life span that has no relation to another type of data's life span. There's no meaningful "average life span of data".
I have the potential of housing a lot of dead data. (data that is never accessed again).
But they will when they perform those reports then they are accessing that data.
Until then you'll need to keep the data in some form. Move to another table or have a switch like you mentioned.
uh...at the risk of oversimplifying...it sounds like using DateDeleted instead of a bit would solve your how-long-to-keep issue.