PERFORMANCE BOOSTERS FOR ETL
PROGRAMS:
Challenges
ETL
processes can involve considerable complexity, and significant operational
problems can occur with improperly designed ETL systems.
The range
of data values or data quality in an operational system may exceed the
expectations of designers at the time validation and transformation rules are
specified. Data profiling of a
source during data analysis can identify the data conditions that will need to
be managed by transform rules specifications. This will lead to an amendment of
validation rules explicitly and implicitly implemented in the ETL process.
Data
warehouses are typically assembled from a variety of data sources with
different formats and purposes. As such, ETL is a key process to bring all the
data together in a standard, homogeneous environment.
Design
analysts should establish the scalability of an
ETL system across the lifetime of its usage. This includes understanding the
volumes of data that will have to be processed within service
level agreements. The time available to extract from source systems
may change, which may mean the same amount of data may have to be processed in
less time. Some ETL systems have to scale to process terabytes of data to
update data warehouses with tens of terabytes of data. Increasing volumes of
data may require designs that can scale from daily batch to
multiple-day microbatch to integration with message queues or
real-time change-data capture for continuous transformation and update
Performance
ETL
vendors benchmark their record-systems at multiple TB (terabytes) per hour (or
~1 GB per second) using powerful servers with multiple CPUs, multiple hard
drives, multiple gigabit-network connections, and lots of memory. The fastest
ETL record is currently held by Syncsort,[1] Vertica and HP at 5.4TB in under an
hour which is more than twice as fast as the earlier record held by Microsoft
and Unisys.
In real
life, the slowest part of an ETL process usually occurs in the database load
phase. Databases may perform slowly because they have to take care of
concurrency, integrity maintenance, and indices. Thus, for better performance,
it may make sense to employ:
- Direct
Path Extract method or bulk unload whenever is possible (instead of
querying the database) to reduce the load on source system while getting
high speed extract
- most
of the transformation processing outside of the database
- bulk
load operations whenever possible.
Still,
even using bulk operations, database access is usually the bottleneck in the
ETL process. Some common methods used to increase performance are:
- Partition
tables (and indices). Try to keep partitions similar in size (watch for null values which can skew the partitioning).
- Do
all validation in the ETL layer before the load. Disable integrity checking (disable
constraint ...) in the target database tables during the
load.
- Disable
triggers (disable trigger
...) in the target database tables during the load. Simulate their effect
as a separate step.
- Generate
IDs in the ETL layer (not in the database).
- Drop
the indices (on
a table or partition) before the load - and recreate them after the load
(SQL: drop index ...;
create index ...).
- Use
parallel bulk load when possible — works well when the table is
partitioned or there are no indices. Note: attempt to do parallel loads
into the same table (partition) usually causes locks — if not on the data
rows, then on indices.
- If a
requirement exists to do insertions, updates, or deletions, find out which
rows should be processed in which way in the ETL layer, and then process
these three operations in the database separately. You often can do bulk
load for inserts, but updates and deletes commonly go through an API
(using SQL).
Whether
to do certain operations in the database or outside may involve a trade-off.
For example, removing duplicates using distinct may be
slow in the database; thus, it makes sense to do it outside. On the other side,
if using distinct will significantly (x100) decrease the number of
rows to be extracted, then it makes sense to remove duplications as early as
possible in the database before unloading data.
A common
source of problems in ETL is a big number of dependencies among ETL jobs. For
example, job "B" cannot start while job "A" is not
finished. You can usually achieve better performance by visualizing all
processes on a graph, and trying to reduce the graph making maximum use of parallelism, and
making "chains" of consecutive processing as short as possible.
Again, partitioning of big tables and of their indices can really help.
Another
common issue occurs when the data is spread between several databases, and processing
is done in those databases sequentially. Sometimes database replication may be
involved as a method of copying data between databases - and this can
significantly slow down the whole process. The common solution is to reduce the
processing graph to only three layers:
- Sources
- Central
ETL layer
- Targets
This
allows processing to take maximum advantage of parallel processing. For
example, if you need to load data into two databases, you can run the loads in
parallel (instead of loading into 1st - and then replicating into the 2nd).
Of
course, sometimes processing must take place sequentially. For example, you
usually need to get dimensional (reference) data before you can get and
validate the rows for main "fact"
tables.
Parallel processing
A recent development in ETL software is the implementation
of parallel
processing. This has enabled a number of methods to improve
overall performance of ETL processes when dealing with large volumes of data.
ETL
applications implement three main types of parallelism:
- Data: By
splitting a single sequential file into smaller data files to provide parallel access.
- Pipeline:
Allowing the simultaneous running of several components on the same data stream. For example: looking up a value on record 1
at the same time as adding two fields on record 2.
- Component:
The simultaneous running of multiple processes on
different data streams in the same job, for example, sorting one input
file while removing duplicates on another file.
All three
types of parallelism usually operate combined in a single job.
An
additional difficulty comes with making sure that the data being uploaded is
relatively consistent. Because multiple source databases may have different
update cycles (some may be updated every few minutes, while others may take
days or weeks), an ETL system may be required to hold back certain data until
all sources are synchronized. Likewise, where a warehouse may have to be
reconciled to the contents in a source system or with the general ledger,
establishing synchronization and reconciliation points becomes necessary.
Source: Wikipedia.
1
US
$421,849
2
France
$121,519
3
UK
$94,244
4
Germany
$89,311
5
US
$82,189
Operations
Retail pricing
The pricing technique used by most retailers is cost-plus pricing. This involves adding a markup amount (or percentage) to the retailer's cost. Another common technique is suggested retail pricing. This simply involves charging the amount suggested by the manufacturer and usually printed on the product by the manufacturer.In Western countries, retail prices are often called psychological prices or odd prices. Often prices are fixed and displayed on signs or labels. Alternatively, when prices are not clearly displayed, there can be price discrimination, where the sale price is dependent upon who the customer is. For example, a customer may have to pay more if the seller determines that he or she is willing and/or able to. Another example would be the practice of discounting for youths, students, or senior citizens..
Staffing
Because patronage at a retail outlet varies flexibility in scheduling is desirable. Employee scheduling software is sold which, using known patterns of customer patronage, more or less reliably predicts the need for staffing for various functions at times of the year, day of the month or week, and time of day. Usually needs vary widely. Conforming staff utilization to staffing needs requires a flexible workforce which is available when needed but does not have to be paid when they are not, part-time workers; as of 2012 70% of retail workers in the United States were part-time. This may result in financial problems for the workers, who while they are required to be available at all times if their work hours are to be maximized, may not have sufficient income to meet their family and other obligations.[3]Transfer mechanisms
There are several ways in which consumers can receive goods from a retailer:
- Counter service, where goods are out of reach of buyers and must be obtained from the seller. This type of retail is common for small expensive items (e.g. jewelry) and controlled items like medicine and liquor. It was common before the 1900s in the United States and is more common in certain countries like India.[which?]
- Delivery, where goods are shipped directly to consumer's homes or workplaces. Mail order from a printed catalog was invented in 1744 and was common in the late 19th and early 20th centuries. Ordering by telephone is now common, either from a catalog, newspaper, television advertisement or a local restaurant menu, for immediate service (especially for pizza delivery). Direct marketing, including telemarketing and television shopping channels, are also used to generate telephone orders. started gaining significant market share in developed countries in the 2000s.
- Door-to-door sales, where the salesperson sometimes travels with the goods for sale.
- Self-service, where goods may be handled and examined prior to purchase
Source: Wikipedia.
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