Data Warehouse Capacity Planning

Data Warehouse Capacity Planning : Data Warehouse Capacity Planning is an essential part of managing and optimizing your data storage. It involves predicting future data volume, determining the necessary infrastructure, and making provisions for growth. An effective plan ensures that your data warehouse has enough space to store data efficiently while minimizing costs. Capacity planning Any data warehouse solution will grow over time sometimes quite dramatically It is essential that the components of the solution (hardware, software, and database) are capable ofsupporting the extended sizes without unacceptable performance loss, or growth of the load window to a point where it affects the use of the system The capacity plan for a data warehouse is defined within the technical blueprint stage of the process The business requirements stage should have identified the approximate sizes for data, users, and any other issues that constrain system performance Capacity Required By The Hardware have a clear understanding of the usage profiles of all users of the data warehouse like the number of users in the group; whether they use ad hoc queries frequently; whether they use ad hoc queries occasionally at unknown intervals whether they use ad hoc queries occasionally at regular and predictable times; the average size of query they tend to run; the maximum size of query they tend to run; the elapsed login time per day; the peak time of daily usage; the number of queries they.run per peak hour; the number of queries they run per day Usage profiles change over time, and need to be kept up to date useful for growth predictions and capacity planning require an understanding of the business Usage profiles change over time, and need to be kept up to date useful for growth predictions and capacity planning require an understanding of the business Understanding the Necessity of Data Warehouse Capacity Planning Capacity planning is vital for the efficient operation and management of a data warehouse. Without proper planning, organizations risk facing issues such as running out of storage space, poor performance, and increased costs. The process involves assessing the current data volume, estimating future data growth, and ensuring the infrastructure can accommodate this growth. This foresight allows organizations to avoid unnecessary expenses and disruptions that may arise from unexpected data spikes. Additionally, capacity planning aids in optimizing the performance of data warehouse systems. By accurately predicting the storage requirements, organizations can ensure that their systems are neither over-provisioned nor under-provisioned. This balance helps to maintain optimal performance levels and prevents wastage of resources. Therefore, capacity planning is not just about managing storage space, but also about enhancing the overall efficiency of data warehouse systems. The Process of Data Warehouse Capacity Planning Capacity planning starts with understanding the organization's data needs. This involves analyzing the current data volume, the rate of data growth, and the types of data being stored. It's also important to consider the organization's future plans, such as expansion or diversification, which could affect data volumes. Once these factors are understood, the next step is to evaluate the existing infrastructure to determine if it can handle the anticipated data growth. After evaluating the data needs and the existing infrastructure, the next step is to develop a capacity plan. This plan should outline the required infrastructure upgrades or expansions, the estimated costs, and the timeline for implementation. It's important to regularly review and update the capacity plan to accommodate changes in the organization's data needs or business strategy. Tools for Data Warehouse Capacity Planning There are various tools available to assist with capacity planning. These tools can analyze data usage patterns, predict future data growth, and recommend infrastructure upgrades or expansions. Some tools also provide simulation capabilities, allowing organizations to test their capacity plans before implementation. The choice of tool often depends on the complexity of the organization's data needs and the scale of its data warehouse. Regardless of the tools used, it's vital to remember that capacity planning is not a one-time activity. It's a continuous process that requires regular reviews and updates. This ongoing effort helps to ensure that the data warehouse can always accommodate the organization's data needs, while maintaining optimal performance and minimizing costs. Challenges in Data Warehouse Capacity Planning The process of data warehouse capacity planning is not without its challenges. One of the main challenges is accurately predicting future data growth. This is often difficult due to the unpredictable nature of data generation and consumption. Factors such as changes in business strategy, market trends, and technological advancements can significantly affect data volumes. Another challenge is balancing cost and performance. While it's important to ensure that the data warehouse has enough capacity to handle data growth, it's also crucial to avoid over-provisioning, which can lead to unnecessary costs. On the other hand, under-provisioning can result in poor performance and disruptions in service. Therefore, achieving the right balance is a complex task that requires careful planning and management. Importance of Expertise in Data Warehouse Capacity Planning Given the importance and complexity of capacity planning, it's crucial to have the right expertise in place. Having a team with a deep understanding of data warehousing and capacity planning can greatly enhance the effectiveness of the process. Such a team can accurately assess the organization's data needs, develop a robust capacity plan, and manage the implementation of the plan. Moreover, the expertise in data warehousing and capacity planning is not just about technical knowledge. It also involves understanding the organization's business strategy, market trends, and technological advancements. This business acumen allows the team to make informed decisions about capacity planning, ensuring that the data warehouse is always aligned with the organization's goals and objectives. Please read this disclaimer carefully before you start to use the service. By using the service, you acknowledge that you have agreed to and accepted the content of this disclaimer in full. You may choose not to use the service if you do not agree to this disclaimer. 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PROCESS It have a clear understanding of the usage profiles of all users of the data warehouse like ? the number of users in the group; ? whether they use ad hoc queries frequently; ? whether they use ad hoc queries occasionally at unknown intervals ? whether they use ad hoc queries occasionally at regular and predictable times; ? the average size of query they tend to run; ? the maximum size of query they tend to run; ? the elapsed login time per day; ? the peak time of daily usage; ? the number of queries they run per peak hour; ? the number of queries they run per day Usage profiles: ? change over time, and need to be kept up to date ? useful for growth predictions and capacity planning ? require an understanding of the business Estimating the load Factors for choosing the hardware: hardware architecture, resilience (flexibility) consideration of the ultimate size of the data warehouse extremely costly to change hardware in midstream different elements that need to be considered like how much CPU, how much memory how much disk The cost should never be allowed to affect capacity planning A small growth in requirements in a data war ehouse environment can mean adding enough hardware to run a full-sized operational OLTP system Example: adding a single byte to a field in the fact data meant adding 4GB of storage for each year's worth of online data Rules / Steps in Estimating the load Initial Configuration How Much CPU Bandwidth How Much Memory How Much Disk Initial Configuration will not have history information or statistics need to be done on the predicted load difficult, due to an ad hoc element phased approach: ad hoc element will be small to start off, functionality is phased in estimate the configuration based on the known requirements need to allow some Contingency requirements are often difficult to pin down accurately, the load can quickly vary from the expected How Much CPU Bandwidth the load can be divided into two distinct phases: daily processing user query processing overnight processing data transformation and load aggregation and index creation backup Daily Processing estimate the time that each query will take estimate the size of the largest likely common query o example: users will want to query the most recent week or month's worth of data know the volume of data that will be involved o assume that the query will perform a full table scan of the fact data for that period need to know the I/O characteristics of the devices that the data will reside.on Using S (scan rate) and F (volume of data) you can calculate T, the time in seconds to perform a full table scan as T = F / S depending on the degree of parallelism, we get T1 = F / S1, …, Tn =F/ Sn where S1 is the scan speed of a single disk or striped disk set, and Sn is the scan speed of all the disks or disk sets take the query response time requirements specified in the service level agreement and pick the appropriate T value, Tp this will give you Sp - required scan rate the number of disks or disk sets, P, the required degree of parallelism need to estimate Pc, the number of parallel scanning threads that a single CPU will support you can estimate your CPU requirement to support a single query with need to use 2P to allow for other query overheads, sorts etc . To calculate the minimum number of CPUs required overall use the formula where n is the number of concurrent queries additional 1 is added to allow for the operating system overheads and for all other user processing. operations like data transformation and load, aggregation and index creation and backup are serialized. they tend to run one at a time, one after the other. Data transformation Depend on the amount of data processing that is involved will require more CPU bandwidth than the aggregation and index creation operations when there is an enormous amount of data transformation. Backup Backup will fit into the CPU bandwidth required by the aggregation and index creation backup use of many parallel streams to speed up the amount of parallelism will come down for longer backups, and its CPU bandwidth requirement will drop. Data load Data load can use massive parallelism to speed up its operation if you use fewer parallel streams it will use less CPU bandwidth.and will take longer to run likely to fit into whatever CPU capacity is required for the aggregation and index creation postprocessing to be carried out on the loaded data. Allowed time Delays in data arrival can cause you significant problems . make sure that you can complete the overnight processing without running over into the business day . 60% available to complete the postprocessing of the data and the aggregation and index creation . How Much Memory need to estimate is the minimum requirement . things that affect the amount of memory required are as follows . Database requirements database will need memory to cache data blocks as they are used to cache parsed SQL statements These requirements will vary from RDBMS to RDBMS . Each process that performs a sort will require an amount of sort area parallel technology, can have multiple concurrent queries running each user connected to the system will use an amount of memory depend on how they are connected to the system and what software they are running Operating system requirement the operating system will require an amount of memory. will vary with the operating system and the features and tools you are running. Database requirements administration o fact and dimension data aggregations Non-database requirements operating system requirements other software requirements data warehouse requirements o user requirements Guideline Remember to allow for resilience in your disk.space capacity planning Any mirroring or RAID 5 overheads need to be counted . System administration requirements Fact and dimension data Aggregations Indexes System administration requirements data dictionary journal files rollback space temporary requirements temporary space T=(2n + 1) P where n is the number of concurrent queries allowed, and P is the size of a partition Fact and dimension data data that you can actually size Do not attempt to size the database until the database schema is complete Allow for period variations when sizing partitions When calculating the actual size you will need to know the average size of the data in each field; the percentage occupancy of each field; the position of each data field in the table; the RDBMS storage format of each data type; any table, row and column overheads. empty fields can still take up space in a database record database block or page size . difference between using a 2 kB block size and using a 16 kB block size size of the index space required for the fact and dimension data amount of data that you intend to keep online Aggregations allow the same amount of space for aggregations as you will have fact data online Indexes allow as much space again for indexing as for the aggregates themselves Space required by the database will be F is the size of the fact data Fi is the size of the fact data indexation D is the size of the dimension data; Di is the size of the dimension data indexation; A is the size of the aggregations; Ai is the size of the aggregations indexation; T is the size of the database temporary or sort area S is the database system administration overhead take the server environment into account in a cluster or MPP environment, allow space on each node example, the operating system will need to be installed on each node make allowance for swap space, leave enough room for system crash dumps other software that has disk space requirements RDBMS software, any other third-party tools room for the log and trace files space for all the code and tools log and trace space for each of the managers: load, warehouse, and query The load manager need disk space allocated for the source files The users will require disk space In client-server mode of connection, space to c reate data extracts or reports in direct connection, users may also want directories to save SQL queries and other work Data Warehousing - Tuning Data Warehousing - Tuning A data warehouse keeps evolving and it is unpredictable what query the user is going to post in the future. Therefore it becomes more difficult to tune a data warehouse system. So it is needed to tune the different aspects of a data warehouse such as performance, data load, queries, etc. Assessing Performance Tuning The Data Load Tuning Queries Difficulties in Data Warehouse Tuning Tuning a data warehouse is a difficult procedure due to following reasons – • Data warehouse is dynamic; it never remains constant. • It is very difficult to predict what query the user is going to post in the future. • Business requirements change with time. • Users and their profiles keep changing. • The user can switch from one group to another. • The data load on the warehouse also changes with time. Performance Assessment/Assessing performance Here is a list of objective measures of performance – • Average query response time • Scan rates • Time used per day query • Memory usage per process • I/O throughput rates target measures specified in the service level agreement (SLA) tuning beyond requirements can prove counterproductive Example Unreasonable: all queries to complete in less than 3 minutes Reasonable: 90% of all queries against recent data to complete in under 5 minutes Ensure that everyone, from the data warehouse designers to the users and senior management has reasonable performance expectations throw hardware at the problem If the problem is an I/O bottleneck, adding CPU makes the problem worse rather than better Scaling of queries can be addressed by hardware as data size increases add extra hardware to support extra users running the same sort of load ask for certain hardware guarantees guarantees for I/O throughput, scan rates, and processing capacity Performance Assessment Following are the points to remember. • It is necessary to specify the measures in service level agreement (SLA). • It is of no use trying to tune response time, if they are already better than those required. • It is essential to have realistic expectations while making performance assessment. • It is also essential that the users have feasible expectations. • To hide the complexity of the system from the user, aggregations and views should be used. • It is also possible that the user can write a query you had not tuned for. Data Warehousing Tuning : Data Load Tuning Data Warehousing Tuning : Data Load Tuning Data load is a critical part of overnight processing. Nothing else can run until data load is complete. This is the entry point into the system. • The very common approach is to insert data using the SQL Layer. In this approach, normal checks and constraints need to be performed. When the data is inserted into the table, the code will run to check for enough space to insert the data. If sufficient space is not available, then more space may have to be allocated to these tables. These checks take time to perform and are costly to CPU. • The second approach is to bypass all these checks and constraints and place the data directly into the preformatted blocks. These blocks are later written to the database. It is faster than the first approach, but it can work only with whole blocks of data. This can lead to some space wastage. • The third approach is that while loading the data into the table that already contains the table, we can maintain indexes. • The fourth approach says that to load the data in tables that already contain data, drop the indexes & recreate them when the data load is complete. The choice between the third and the fourth approach depends on how much data is already loaded and how many indexes need to be rebuilt. Integrity Checks : Integrity checking highly affects the performance of the load. Following are the points to remember – • Integrity checks need to be limited because they require heavy processing power. • Integrity checks should be applied on the source system to avoid performance degrade of data load. Tuning Queries We have two kinds of queries in data warehouse – • Fixed queries • Ad hoc queries Tuning Queries : Fixed queries Fixed queries are well defined. Following are the examples of fixed queries – • Regular reports • Canned queries • Common aggregations Tuning the fixed queries in a data warehouse is same as in a relational database system. The only difference is that the amount of data to be queried may be different. It is good to store the most successful execution plan while testing fixed queries. Storing these executing plan will allow us to spot changing data size and data skew, as it will cause the execution plan to change. Tuning Queries : Ad hoc Queries To understand ad hoc queries, it is important to know the ad hoc users of the data warehouse. For each user or group of users, you need to know the following – • The number of users in the group • Whether they use ad hoc queries at regular intervals of time • Whether they use ad hoc queries frequently •Whether they use ad hoc queries occasionally at unknown intervals. • The maximum size of query they tend to run • The average size of query they tend to run • Whether they require drill-down access to the base data • The elapsed login time per day • The peak time of daily usage • The number of queries they run per peak hour. Points to Note • It is important to track the user's profiles and identify the queries that are run on a regular basis. • It is also important that the tuning performed does not affect the performance. • Identify similar and ad hoc queries that are frequently run. • If these queries are identified, then the database will change and new indexes can be added for those queries. • If these queries are identified, then new aggregations can be created specifically for those queries that would result in their efficient execution.