Posts tagged with: Database

What I've learned today : Sharding Database

Sharding database consept was founded before a decade however implementations have occurred with complex social networking apps and SaaS and actually it was coined by Google pratically.

Database Sharding can be simply defined as a “shared-nothing” partitioning scheme for large databases across a number of servers, enabling new levels of database performance and scalability achievable. If you think of broken glass, you can get the concept of sharding – breaking your database down into smaller chunks called “shards” and spreading those across a number of distributed servers.

The basic concept of Database Sharding is very straightforward: take a large database, and break it into a number of smaller databases across servers. The concept is illustrated in the following diagram:

Database Sharding

Figure 2. Database Sharding takes large databases and breaks them down into smaller databases.

The obvious advantage of the shared-nothing Database Sharding approach is improved scalability, growing in a near-linear fashion as more servers are added to the network. However, there are several other advantages of smaller databases, which should not be overlooked when considering a sharding solution:

  • Smaller databases are easier to manage. Production databases must be fully managed for regular backups, database optimization and other common tasks. With a single large database these routine tasks can be very difficult to accomplish, if only in terms of the time window required for completion. Routine table and index optimizations can stretch to hours or days, in some cases making regular maintenance infeasible. By using the sharding approach, each individual “shard” can be maintained independently, providing a far more manageable scenario, performing such maintenance tasks in parallel.
  • Smaller databases are faster. The scalability of sharding is apparent, achieved through the distribution of processing across multiple shards and servers in the network. What is less apparent is the fact that each individual shard database will outperform a single large database due to its smaller size. By hosting each shard database on its own server, the ratio between memory and data on disk is greatly improved, thereby reducing disk I/O. This results in less contention for resources, greater join performance, faster index searches, and fewer database locks. Therefore, not only can a sharded system scale to new levels of capacity, individual transaction performance is benefited as well.
  • Database Sharding can reduce costs. Most Database Sharding implementations take advantage of lower-cost open source databases, or can even take advantage of “workgroup” versions of commercial databases. Additionally, sharding works well with commodity multi-core server hardware, far less expensive than high-end multi-CPU servers and expensive SANs. The overall reduction in cost due to savings in license fees, software maintenance and hardware investment is substantial, in some cases 70% or more when compared to other solutions.

There is no doubt that Database Sharding is a viable solution for many organizations, supported by the number of large online vendors and SaaS organizations that have implemented the technology (giants such as Amazon, eBay, and of course Google).

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Normalize Your Database with NF rules.

The Normal Form rules are the basic rules that are created by the Relational Database concept creater Edgar F. Codd. The main purpose of these is to create database tables that do not have redundancy for the data inside. There are basically three NF variations 1NF, 2NF, 3NF (Actually now on it goes up to 6NF by other theoreticians but basic structure is based on 1NF 2NF 3NF) .

1NF points out:
– Table should not have columns that are based on same data
– Create separate tables for each group of related data and identify each row with a unique column (the primary key).

Example:
(Manager, Employee1, Employee2 ) are the columns but there are two Employee column that violate rule one.

(Manager, Employee) is good solution but it violates still second rule.

(Manager, EmployeeID) is intuitive solution since each Employee has only one Manager. Now our table is proper for 1NF.

2NF points out:
– Remove subset of data that causes multiple rows includes same information and create seperate table for them
-Use foreign keys to relate the tables that are newly created and previous one

EXAMPLE:
(EmpID,FirstName, LastName, City, Zipcode, ) is our table but if there are two employee with same city so zipcodem it makes two row with redundant data. So it is more appropriate to use another table that includes cities and correspondence zipcodes. In addition new new structure makes easier to add, remove or update address information. Instead of updating all the rows that have same city and zipcode, we just need to update one row in the new table.
(EmpID, FirstName, LastName, AddID) (AddID, City, ZipCode)

3NF points out:
-Each attribute in the table must be fully determined by a key, separate other columns to other table.

EXAMPLE:
(CustomerID, OrderNum, UnitPrice, Quantity, Total) in this table all the attributes are fully depended to CustomerID but the Total. Total is determined by the UnitPrice*Quantity so it is not fully depended to key. It can be deleted.
(CustomerID, OrderNum, UnitPrice, Quantity) is new table

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