I'm developing a statistics module for my website that will help me measure conversion rates, and other interesting data.
The mechanism I use is - to store a database entry in a statistics table - each time a user enters a specific zone in my DB (I avoid duplicate records with the help of cookies).
For example, I have the following zones:
Website - a general zone used to count unique users as I stopped trusting Google Analytics lately.
Category - self descriptive.
Minisite - self descriptive.
Product Image - whenever user sees a product and the lead submission form.
Problem is after a month, my statistics table is packed with a lot of rows, and the ASP.NET pages I wrote to parse the data load really slow.
I thought maybe writing a service that will somehow parse the data, but I can't see any way to do that without losing flexibility.
My questions:
How large scale data parsing applications - like Google Analytics load the data so fast?
What is the best way for me to do it?
Maybe my DB design is wrong and I should store the data in only one table?
Thanks for anyone that helps,
Eytan.
The basic approach you're looking for is called aggregation.
You are interested in certain function calculated over your data and instead of calculating the data "online" when starting up the displaying website, you calculate them offline, either via a batch process in the night or incrementally when the log record is written.
A simple enhancement would be to store counts per user/session, instead of storing every hit and counting them. That would reduce your analytic processing requirements by a factor in the order of the hits per session. Of course it would increase processing costs when inserting log entries.
Another kind of aggregation is called online analytical processing, which only aggregates along some dimensions of your data and lets users aggregate the other dimensions in a browsing mode. This trades off performance, storage and flexibility.
It seems like you could do well by using two databases. One is for transactional data and it handles all of the INSERT statements. The other is for reporting and handles all of your query requests.
You can index the snot out of the reporting database, and/or denormalize the data so fewer joins are used in the queries. Periodically export data from the transaction database to the reporting database. This act will improve the reporting response time along with the aggregation ideas mentioned earlier.
Another trick to know is partitioning. Look up how that's done in the database of your choice - but basically the idea is that you tell your database to keep a table partitioned into several subtables, each with an identical definition, based on some value.
In your case, what is very useful is "range partitioning" -- choosing the partition based on a range into which a value falls into. If you partition by date range, you can create separate sub-tables for each week (or each day, or each month -- depends on how you use your data and how much of it there is).
This means that if you specify a date range when you issue a query, the data that is outside that range will not even be considered; that can lead to very significant time savings, even better than an index (an index has to consider every row, so it will grow with your data; a partition is one per day).
This makes both online queries (ones issued when you hit your ASP page), and the aggregation queries you use to pre-calculate necessary statistics, much faster.
Related
Is there any good documentation on how query times change for a DynamoDB table based on equal read capacity and differing row sizes? I've been reading through the documentation and can't find anything, was wondering if anybody has done any studies into this?
My use case is that I'm putting a million rows into a table a week. These records are referenced quite a bit as they're entered but as time goes on the frequency at which I query those rows decreases. Can I leave those records in the table indefinitely with no detrimental effect on query time, or should I rotate them out so the newer data that is requested more frequently returns faster?
Please don't keep the old data indefinitely. It is advised to archive the data for better performance.
Few points on design and testing:-
Designing the proper hash key, so that the data is distributed
access the partitions
Understand Access Patterns for Time Series Data
Test your application at scale to avoid problems with "hot" keys
when your table becomes larger
Suppose you design a table to track customer behavior on your site,
such as URLs that they click. You might design the table with a
composite primary key consisting of Customer ID as the partition key
and date/time as the sort key. In this application, customer data
grows indefinitely over time; however, the applications might show
uneven access pattern across all the items in the table where the
latest customer data is more relevant and your application might
access the latest items more frequently and as time passes these items
are less accessed, eventually the older items are rarely accessed. If
this is a known access pattern, you could take it into consideration
when designing your table schema. Instead of storing all items in a
single table, you could use multiple tables to store these items. For
example, you could create tables to store monthly or weekly data. For
the table storing data from the latest month or week, where data
access rate is high, request higher throughput and for tables storing
older data, you could dial down the throughput and save on resources.
Time Series Data Access Pattern
Guidelines for table partitions
I'm building an application in ASP.NET(VB) with a MS SQL database. It is a search tool for cars that has a list of every car and all of their attributes (colors, # of doors, gas milage, mfg. year, etc). This tool outputs the results in a gridview and the users has the ability to perform advanced searches and filtering. The filtering needs to be very fine-grained (range of gas milage, color(s), mfg year range, etc.) and I cannot seem to find the best way to do this filtering without a large SQL where statement that is going to greatly impact SQL performance and page load. I feel like I'm missing something very obvious here, thank you for any help. I'm not sure what other details would be helpful.
This is not an OLTP database you're building--it's really an analytics database. There really isn't a way around the problem of having to filter. The question is whether the organization of the data will allow seeks most of the time, or will it require scans; and also whether the resulting JOINs can be done efficiently or not.
My recommendation is to go ahead and create the data normalized and all, as you are doing. Then, build a process that spins it into a data warehouse, denormalizing like crazy as needed, so that you can do filtering by WHERE clauses that have to do a lot less work.
For every single possible search result, you have a row in a table that doesn't require joining to other tables (or only a few fact tables).
You can reduce complexity a bit for some values such as gas mileage, by striping the mileage into bands of, say, 5 mpg. (10-19, 20-24, 25-29, etc.)
As you need to add to the data and change it, your data-warehouse-loading process (that runs once a day perhaps) will keep the data warehouse up to date. If you want more frequent loading that doesn't keep clients offline, you can build the data warehouse to an alternate node, then swap them out. Let's say it takes 2 hours to build. You build for 2 hours to a new database, then swap to the new database, and all your data is only 2 hours old. Then you wipe out the old database and use the space to do it again.
I want to retrieve data from Google Analytics API, create custom calculations and then push the aggregations to a Google Spreadsheets in order to reuse in Google Visualisation API app. My concern is that I'll hit the Spreadsheet cell quota very quickly with the raw data needed for the calculation.
I know scriptDB quota is 100MB but before I invest time and resources in learning how it works I'd like to get an idea whether it's feasible for storing raw analytics data (provided it's not too granular and it's just designed to answer specific questions) and how much of it I could realistically store in scriptDB (relative to spreadsheets) before I hit the quota.
Thanks
For bulk data access (e.g. reading a table for Visualization), a spreadsheet will have a speed advantage over ScriptDb. What is faster: ScriptDb or SpreadsheetApp? If you wish to support more sophisticated queries though, to "answer specific questions" as you mention, then ScriptDb will give you an edge, as query times vary with the number of results but should be unaffected by the query criteria themselves.
With data in a spreadsheet, you will be able to obtain a DataTable for Visualization with a single Range.getDataTable() operation. With ScriptDb, you will need to write a script to build your DataTable.
Regarding size constraints, it's not possible to really compare the two without knowing the size of your individual data elements. You're already aware of the general constraints:
Spreadsheet, 40K cells, but may hit (unspecified) size limit before that, depending on data element sizes.
ScriptDb, 50MB, 100MB or 200MB depending on account type. The number of objects that can be stored is affected by the complexity (depth) of the object and the size of the property names, and of course the size of data contained in the objects.
Ultimately, the question of which is best for your application is a matter of opinion, and of which factors matter most for the application. If the analytics data is tabular, then a spreadsheet offers advantages for implementation largely because of Range.getDataTable(), and is faster for bulk access. I'd recommend starting there, and considering a move to ScriptDb if and when you actually hit spreadsheet size or query performance limitations.
I am building UI for a large product catalog (millions of products).
I am using Sql Server, FreeText search and ASP.NET MVC.
Tables are normalized and indexed. Most queries take less then a second to return.
The issue is this. Let's say user does the search by keyword. On search results page I need to display/query for:
Display 20 matching products on first page(paged, sorted)
Total count of matching products for paging
List of stores only of all matching products
List of brands only of all matching products
List of colors only of all matching products
Each query takes about .5 to 1 seconds. Altogether it is like 5 seconds.
I would like to get the whole page to load under 1 second.
There are several approaches:
Optimize queries even more. I already spent a lot of time on this one, so not sure it can be pushed further.
Load products first, then load the rest of the information using AJAX. More like a workaround. Will need to revise UI.
Re-organize data to be more Report friendly. Already aggregated a lot of fields.
I checked out several similar sites. For ex. zappos.com. Not only they display the same information as I would like in under 1 second, but they also include statistics (number of results in each category).
The following is the search for keyword "white"
http://www.zappos.com/white
How do sites like zappos, amazon make their results, filters and stats appear almost instantly?
So you asked specifically "how does Zappos.com do this". Here is the answer from our Search team.
An alternative idea for your issue would be using a search index such as solr. Basically, the way these work is you load your data set into the system and it does a huge amount of indexing. My projects include product catalogs with 200+ data points for each of the 140k products. The average return time is less than 20ms.
The search indexing system I would recommend is Solr which is based on lucene. Both of these projects are open source and free to use.
Solr fits perfectly for your described use case in that it can actually do all of those things all in one query. You can use facets (essentially group by in sql) to return the list of different data values for all applicable results. In the case of keywords it also would allow you to search across multiple fields in one query without performance degradation.
you could try replacing you aggergate queries with materialized indexed views of those aggregates. this will pre-compute all the aggregates and will be as fast as selecting any regular row data.
.5 sec is too long for an appropriate hardware. I agree with Aaronaught and first thing to do is to convert it in single SQL or possibly Stored Procedure to ensure it's compiled only once.
Analyze your queries to see if you can create even better indexes (consider covering indexes), fine tune existing indexes, employ partitioning.
Make sure you have appropriate hardware config - data, log, temp and even index files should be located on independent spindles. make sure you have enough RAM and CPU's. I hope you are running 64-bit platform.
After all this, if you still need more - analyze most used keywords and create aggregate result tables for top 10 keywords.
Amount Amazon - they most likely use superior hardware and also take advantage of CDN's. Also, they have thousands of servers surviving up the content and there is no performance bottlenecks - data is duplicated multiple times across several data centers.
As completely separate approach - you may want to look into "in-memory" databases such as CACHE - this is the fastest you can get on DB side.
What are the pros/cons of de-normalizing an enterprise application database because it will make writing reports easier?
Pro - designing reports in SSRS will probably be "easier" since no joins will be necessary.
Con - developing/maintaining the app to handle de-normalized data will become more difficult due to duplication of data and synchronization.
Others?
Denormalization for the sake of reports is Bad, m'kay.
Creating views, or a denormalized data warehouse is good.
Views have solved most of my reporting related needs. Data warehouses are great when users will be generating reports almost constantly or when your views start to slow down.
This is why you want to normalize your database
To free the collection of relations from undesirable insertion, update and deletion dependencies;
To reduce the need for restructuring the collection of relations as new types of data are introduced, and thus increase the life span of application programs;
To make the relational model more informative to users;
To make the collection of relations neutral to the query statistics, where these statistics are liable to change as time goes by.
—E.F. Codd, "Further Normalization of the Data Base Relational Model" via wikipedia
The only time you should consider de-normaliozation is when the time it takes the report to generate is not acceptable. De-normalization will cause consistentcy issues that are sometimes impossible to determine especially in large datasets
Don't denormalize just to get rid of complexity in reporting, it can cause huge problems in the rest of the application. Either you don't enforce the rules resulting in bad data or if you do then inserts, deletes and updates can be seriously slowed for everyone not just the two or three people who run reports.
If the reports truly can't run well, then create a data warehouse that is denormalized and populate it in a nightly or weekly feed. The kind of reports that typically need this do not generally care if the data is up-to-the minute as they are usually monthly, quarterly, or annual reports that process (and especially aggregate) large amounts of data after the fact.
You can do both... let the normalized database for applications.
Then create a denormalized database for reports, and create an application which regulary copy data from one database to the other.
After all, reports don't always need to have the latest updated data, most of the time you can easily launch an update every 1 hour on the reporting database, and only once a day day.
Beyond the data warehouse and views solutions provided in other answers, which are good in some ways, if you are willing to sacrifice some performance to get a good to the last second data, but still want a normalized database, you could use on Oracle a Materialized View with fast refresh on commit, or in Sql Server, you could use clustered indexes for a view.
Another Con is that the data is likely not to be real-time as there is some time moving around the data to go from a normalized form to a de-normalized. If someone wants the report to be up to the very second it was requested, that can be tough to do in this situation.
If this is a duplication of the synchronization in the original post, sorry I didn't quite see it that way.