How to estimate how much data your Greenplum Database system can accommodate
Post date: Sep 21, 2012 12:47:47 PM
Q. How to estimate how much data your Greenplum Database system can accommodate
Ans: Use the following as guidelines. You may want to have extra space for landing backup files and data load files on each segment host.
Calculating Usable Disk Capacity
To calculate how much data a Greenplum system can hold, you have to calculate the usable disk capacity per segment host and then multiply that by the number of segment hosts in your Greenplum array. Start with the raw capacity of the physical disks on a segment host that are available for data storage (raw_capacity), which is:
disk_size * number_of_disks
Account for file system formatting overhead (roughly 10 percent) and the RAID level you are using. For example, if using RAID-10, the calculation would be:
(raw_capacity * 0.9) / 2 = formatted_disk_space
For optimal performance, Greenplum recommends that you do not completely fill your disks to capacity, but run at 70% or lower. So with this in mind, calculate the usable disk space as follows:
formatted_disk_space * 0.7 = usable_disk_space
Once you have formatted RAID disk arrays and accounted for the maximum recommended capacity (usable_disk_space), you will need to calculate how much storage is actually available for user data (U). If using Greenplum mirrors for data redundancy, this would then double the size of your user data (2 * U). Greenplum also requires some space be reserved as a working area for active queries. The work space should be approximately one third the size of your user data (work space = U/3):
With mirrors: (2 * U) + U/3 = usable_disk_space
Without mirrors: U + U/3 = usable_disk_space
Calculating User Data Size
As with all databases, the size of your raw data will be slightly larger once it is loaded into the database. On average, raw data will be about 1.4 times larger on disk after it is loaded into the database, but could be smaller or larger depending on the data types you are using, table storage type, in-database compression, and so on.
1. Page Overhead - When your data is loaded into Greenplum Database, it is divided into pages of 32KB each. Each page has 20 bytes of page overhead.
2. Row Overhead - In a regular ‘heap’ storage table, each row of data has 24 bytes of row overhead. An ‘append-only’ storage table has only 4 bytes of row overhead.
3. Attribute Overhead - For the data values itself, the size associated with each attribute value is dependent upon the data type chosen. As a general rule, you want to use the smallest data type possible to store your data (assuming you know the possible values a column will have).
4.Indexes - In Greenplum Database, indexes are distributed across the segment hosts as is table data. The default index type in Greenplum Database is B-tree. Because index size depends on the number of unique values in the index and the data to be inserted, precalculating the exact size of an index is impossible. However, you can roughly estimate the size of an index using these formulas.
B-tree: unique_values * (data_type_size + 24 bytes)
Bitmap: (unique_values * number_of_rows * 1 bit * compression_ratio / 8) + (unique_values * 32)
Calculating Space Requirements for Metadata and Logs
On each segment host, you will also want to account for space for Greenplum Database log files and metadata:
1. System Metadata — For each Greenplum Database segment instance (primary or mirror) or master instance running on a host, estimate approximately 20 MB for the system catalogs and metadata.
2. Write Ahead Log — For each Greenplum Database segment (primary or mirror) or master instance running on a host, allocate space for the write ahead log (WAL). The WAL is divided into segment files of 64 MB each. At most, the number of WAL files will be: 2 * checkpoint_segments + 1 . You can use this to estimate space requirements for WAL. The default checkpoint_segments setting for a Greenplum Database instance is 8, meaning 1088 MB WAL space allocated for each segment or master instance on a host.
3. Greenplum Database Log Files — Each segment instance and the master instance generates database log files, which will grow over time. Sufficient space should be allocated for these log files, and some type of log rotation facility should be used to ensure that to log files do not grow too large.
4. Performance Monitor Data — The Greenplum Performance Monitor agents run on the same set of hosts as your Greenplum Database instance and utilize the system resources of those hosts. The resource consumption of the Greenplum Performance Monitor agent processes on these hosts is minimal and should not significantly impact database performance. Historical data collected by Greenplum Performance Monitor data is stored in its own gpperfmon database within your Greenplum Database system. Collected monitor data is distributed just like regular database data, so you will need to account for disk space in the data directory locations of your Greenplum segment instances. The amount of space required depends on the amount of historical data you would like to keep. Historical data is not automatically truncated. Database administrators must set up a truncation policy to maintain the size of the gpperfmon database.