The document discusses the design of an analytics database to aggregate data from multiple sources, move the aggregated data to frontend databases, and serve queries efficiently through portals. It addresses partitioning the backend warehouse for writes, replicating data to secondaries, moving data to partitioned frontend databases while serving queries, and optimizing queries and indexes at the frontend. Table partitioning is recommended for the frontend to allow efficient data insertion, removal and query serving while mitigating the impact of conflicting I/O operations.

1. Dev Analytics Aggregated DB
Design Analysis

2. Analytics Problem Context
• Broadly the problem can be broken into three parts
1. Aggregate from one or more sources
2. Bring the aggregated data from the BE to FE DBs
3. Serve up data in a performant way through the portals
• Fundamental Requirements
• Backend Warehouse
• That can comfortably process some amount of data in a safe robust manner
• That is highly available
• Frontend DB to serve the queries from the portal
• Should serve queries in an efficient manner
• Must be highly available
• To mitigate the effects of disk performance for concurrent queries, read scale-out is required
• Scale out Model
• The above two are components of a stamp unit which would get replicated as instances to support
further scale out

3. Part 1 : Process data
• What happens?
• Large number of rows get inserted into the mining warehouse, so the CPU/Memory Usage can spike up
• So, we chunk the data to control the resource usage
• Are we setup for efficient writes to the SQL data files ?
• Data and Log file configurations matter
• Exploit Contiguity on disk
• Replication to other secondaries/primaries (for HA) can bring the system to the knees
• So, we chunk the data and provide breathing space
• We need to monitor the replication backlogs
• Are we setup to best contain the replication overheads ?
• Log files play a major role
• Read Efficiencies
• Write Efficiencies
• Spindle separation
• Data Categories
• Data that needs to be strongly consistent across partitions
• Scalability
• Currently we have 2 partitions?
• Reference to a document on the effort required to add another partition?
• Real-time
• Currently we process data once a day
• Current Indexing / Query Design Mantra
• Data comes in time order, prioritize for clustered indexes for write
• For read, rely on scan performance (these need to be validated)

4. Part 1 : Process Data (Cont)
• Data Categories
• Data that needs to be strongly consistent across partitions
• And highly available?
• Data that needs to be replicated
• Checkpoint/Restart Model?
• Scalability
• Currently we have 2 partitions?
• Reference to a document on the effort required to add another partition?
• Real-time
• Currently we process data once a day
• Current Indexing / Query Design Mantra
• Data comes in time order, prioritize for clustered indexes for write
• For read, rely on scan performance (these need to be validated)

5. Part 2: Move Data to FE while it is serving
Data
• What happens
• User queries some application specific or trend data
• While the user query is being processed, new data arrives and is inserted in time order
• This writes are chunked and currently occur over a period of 4 hours
• The writes result in fragmentation of the non clustered index, also new data requires SQL
statistics to be updated
• We have no partitioning in FE so defrag and update statistics are costly operations and
moreover they result in CPU/Memory Usage spikes
• In general, large tables make everything inefficient
• Mantra to write without disrupting the query performance
• Partitioning is highly recommended simply because the fact tables are large
• Table Partitioning
• Note the partitions are in time order, helps purge old data as well
• Active Passive is a poor cousin

6. Part 3: Serving Data
• What happens
• User queries some application specific or trend data
• Trend/Category Wide data requires query of records across applications
• Some queries require processing at the FE because of the nature of the queries
• User is asked to select a filter say over Country(35 choices?), Age(3), Gender (3) and numbers for the top 20 in that category/subcategory are displayed
• Pre-aggregation and sorting of this size/nature also cause DB size bloat
• Feature Use
• For this kind of heavy lifting, it would be useful to determine to what extent this feature is being used
• Queries can be and are disrupted by the writes that occur daily
• Index Mantras
• Rely on Clustered Indexes to the extent possible
• Use few carefully chosen non clustered index as necessary
• Query Mantras
• Intelligent Point Scans
• These serve by removing the need for churning data from time order into application order
• Use of Specific Joins
• Nested Loops Join: http://blogs.msdn.com/craigfr/archive/2006/07/26/679319.aspx
• Hash Join: http://blogs.msdn.com/craigfr/archive/2006/08/10/687630.aspx
• Merge Join: http://blogs.msdn.com/craigfr/archive/2006/08/03/merge-join.aspx
• Be aware/conscious about the size of the tables while designing your sprocs
• Ensure that update statistics is enabled, Auto update is recommended, A forced update after data insertions is desirable
• When not to rely on SQL to make the appropriate choice for you? Ashish?
• Index and Query Reference Spec
• Ashish’s spec

7. Active Passive Solution
• Passive
• Helps control the data insertion process without disruption
• Cons
• Not the best utilization of resources
• Monitoring Intensive
• Certainly not elegant
• Data that is sent to Passive(Set A) on Day 1 has to be fed to the Passive(Set B) on day 2, while
it also receives data for Day 1
• Or robust to backlogs, should they arise
• At this point we only know that at least the FE will continue to serve, though it may serve
stale data in these situations for longer time than we would like

8. Table Partitioning
• General Approach
• Create date-wise partitions with two empty partitions mapped to file-groups
• Empty partitions are used for data insertion and data removal
• Defrag and Update Statistics can be performed on staging partitions created on appropriate file groups
• Since splitting an empty partition is a metadata only operation, it is quick and helps maintain the same configuration
• By appropriate file group separation, you mitigate the impact of conflicting i/o operations
• References
• http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=2&ved=0CC8QFjAB&url=http%3A%2F%2Fdownload.micro
soft.com%2Fdownload%2FD%2FB%2FD%2FDBDE7972-1EB9-470A-BA18-58849DB3EB3B%2FPartTableAndIndexStrat.docx&ei=eKkpUo-
lKIauiQewpYGgCw&usg=AFQjCNHKMusGnaIp9EzsR94YGq8OJRPM1w&bvm=bv.51773540,d.aGc
• http://msdn.microsoft.com/en-us/library/aa964122(SQL.90).aspx
• We believe this is the right long term path for Analytics FE DB evolution
• Even if you offload the BE processing to COSMOS, to serve the portal queries, an FE that allows efficient insertion and removal of data
while servicing requests from the portals is required
• This is an important cog in the design of an optimal and efficient FE unit
• Active/Passive is a stop gap measure with lower utilization of h/w assets
• Need a POC to validate if the query overheads are acceptable (within the desired and acceptable limit)
• References on parallel query execution overheads
• http://technet.microsoft.com/en-us/library/ms345599(v=sql.105).aspx

9. Other General References
• Fast track Recommendations
• http://msdn.microsoft.com/en-us/library/gg567302.aspx
• http://msdn.microsoft.com/en-us/library/gg605238.aspx
• Best practice recommendations
• http://technet.microsoft.com/en-us/library/dd578580(v=sql.100).aspx