Overview of Greenplum Database High Availability

Overview of Greenplum Database High Availability

A Greenplum Database cluster can be made highly available by providing a fault-tolerant hardware platform, by enabling Greenplum Database high-availability features, and by performing regular monitoring and maintenance procedures to ensure the health of all system components.

Hardware components will eventually fail, whether due to normal wear or an unexpected circumstance. Loss of power can lead to temporarily unavailable components. A system can be made highly available by providing redundant standbys for components that can fail so that services can continue uninterrupted when a failure does occur. In some cases, the cost of redundancy is higher than users' tolerance for interruption in service. When this is the case, the goal is to ensure that full service is able to be restored, and can be restored within an expected timeframe.

With Greenplum Database, fault tolerance and data availability is achieved with:

Hardware Level RAID

A best practice Greenplum Database deployment uses hardware level RAID to provide high performance redundancy for single disk failure without having to go into the database level fault tolerance. This provides a lower level of redundancy at the disk level.

Segment Mirroring

Greenplum Database stores data in multiple segments, each of which is a Greenplum Database Postgres instance. The data for each table is spread between the segments based on the distribution policy that is defined for the table in the DDL at the time the table is created. When segment mirroring is enabled, for each segment there is a primary and mirror pair. The primary and mirror perform the same IO operations and store copies of the same data.

The mirror instance for each segment is usually initialized with the gpinitsystem utility or the gpexpand utility. The mirror runs on a different host than the primary instance to protect from a single machine failure. There are different strategies for assigning mirrors to hosts. When choosing the layout of the primaries and mirrors, it is important to consider the failure scenarios to ensure that processing skew is minimized in the case of a single machine failure.

Master Mirroring

There are two masters in a highly available cluster, a primary and a standby. As with segments, the master and standby should be deployed on different hosts so that the cluster can tolerate a single host failure. Clients connect to the primary master and queries can be executed only on the primary master. The secondary master is kept up-to-date by replicating the write-ahead log (WAL) from the primary to the secondary.

If the master fails, the administrator runs the gpactivatestandby utility to have the standby master take over as the new primary master. You can configure a virtual IP address for the master and standby so that client programs do not have to switch to a different network address when the current master changes. If the master host fails, the virtual IP address can be swapped to the actual acting master.

Dual Clusters

An additional level of redundancy can be provided by maintaining two Greenplum Database clusters, both storing the same data.

Two methods for keeping data synchronized on dual clusters are "dual ETL" and "backup/restore."

Dual ETL provides a complete standby cluster with the same data as the primary cluster. ETL (extract, transform, and load) refers to the process of cleansing, transforming, validating, and loading incoming data into a data warehouse. With dual ETL, this process is executed twice in parallel, once on each cluster, and is validated each time. It also allows data to be queried on both clusters, doubling the query throughput. Applications can take advantage of both clusters and also ensure that the ETL is successful and validated on both clusters.

To maintain a dual cluster with the backup/restore method, create backups of the primary cluster and restore them on the secondary cluster. This method takes longer to synchronize data on the secondary cluster than the dual ETL strategy, but requires less application logic to be developed. Populating a second cluster with backups is ideal in use cases where data modifications and ETL are performed daily or less frequently.

Backup and Restore

Making regular backups of the databases is recommended except in cases where the database can be easily regenerated from the source data. Backups should be taken to protect from operational, software, and hardware errors.

Use the gpcrondump utility to backup Greenplum databases. gpcrondomp performs the backup in parallel across segments, so backup performance scales up as hardware is added to the cluster.

When designing a backup strategy, a primary concern is where to store the backup data. The data each segment manages can be backed up on the segment's local storage, but should not be stored there permanently—the backup reduces disk space available to the segment and, more importantly, a hardware failure could simultaneously destroy the segment's live data and the backup. After performing a backup, the backup files should be moved from the primary cluster to separate, safe storage. Alternatively, the backup can be made directly to separate storage.

Additional options are available to backup datatabases, including the following:

Data Domain
Through native API integration backups can be streamed to a Dell EMC Data Domain appliance.
NetBackup
Through native API integration, backups can be streamed to a Veritas NetBackup cluster.
NFS
If an NFS mount is created on each Greenplum Database host in the cluster, backups can be written directly to the NFS mount. A scale out NFS solution is recommended to ensure that backups do not bottleneck on IO throughput of the NFS device. Dell EMC Isilon is an example that can scale out along side the Greenplum cluster.

Incremental Backups

Greenplum Database allows incremental backup at the partition level for append-optimized and column-oriented tables. When you perform an incremental backup, only the partitions for append-optimized and column-oriented tables that have changed since the previous backup are backed up. (Heap tables are always backed up.) Restoring an incremental backup requires restoring the previous full backup and subsequent incremental backups.

Incremental backup is beneficial only when the database contains large, partitioned tables where all but one or a few partitions remain unchanged between backups. An incremental backup saves just the changed partitions and the heap tables. By not backing up the unchanged partitions, the backup size and time can be significantly reduced.

If a large fact table is not partitioned, and a single row is added or changed, the entire table is backed up, and there is no savings in backup size or time. Therefore, incremental backup is only recommended for databases with large, partitioned tables and relatively small dimension tables.

If you maintain dual clusters and use incremental backup, you can populate the second cluster with the incremental backups. Use the --noplan option to achieve this, allowing backups from the primary site to be applied faster.