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Tag: Scale-out Databases

  • A Modern Database Backup and Recovery Checklist

    A Modern Database Backup and Recovery Checklist

    Do you have single points of failure? How do you handle deduplication? These questions and more need consideration when you’re dealing with database backup and recovery.

    Enterprises are quickly onboarding next-generation, mission-critical applications such as eCommerce, artificial intelligence, machine learning and Internet of Things (IoT) on non-relational databases and distributed local storage. However, as organizations move these applications to scalable databases because they are easier to deploy, always available and cost-effective, they also need a robust and scalable backup and recovery solution to protect these modern databases.

    The challenge is that although enterprise adoption of next-generation applications has been rising, the risk of data loss is prevalent. That’s because non-relational databases lack any viable data protection and recovery solution.

    Here is what I mean:

    • Currently, there is a lack of a solution that allows for corrupted data to be removed, replayed and propagated with minimal downtime to customer-facing applications.
    • Reputable studies have concluded that as much as 75 percent of data disasters ultimately leading to downtime are the result of some sort of human error. Because human errors have a big impact on business operations and occur randomly, they cannot be solved by solutions that are offered today, such as native database replication or node-level snapshots.
    • Database replication does not provide point-in-time backup and any-point-in-time recovery, so enterprises cannot go back and fix these errors. In fact, if errors are introduced, the databases’ redundant-node replication can lead to almost immediate corruption across all nodes of the database.

    So, as you begin to explore backup and recovery solution for your next-generation applications, below is a list of items to keep in mind.

    Storage Is No Longer Unified or Shared Storage

    A key architectural transition is occurring where monolithic pools of shared storage are no longer used for next-gen applications and databases. Next-generation databases are scale-out in nature and use commodity storage at a node level, creating a highly distributed storage pool. This distributed storage pool provides both low cost and high performance, but is not conducive to using traditional database backup and recovery tools, which rely on database durability logs (from a shared storage LUN) for a point-in-time backup.

    Eventual Consistency Has a Big Impact on Backup and Recovery

    Next-generation databases are not built around the traditional ACID (Atomicity, Consistency, Isolation, Durability) transaction model. Transactions are a mechanism for guaranteeing these properties; they are a way of grouping related actions together such that as a whole, a group of operations can be atomic, produce consistent results, be isolated from other operations and be durably recorded. Eventual consistency, however, brings forth new challenges to generate a consistent point-in-time view of the database for operational recovery or test/dev needs. Therefore, novel and scale-out data protection technology to address eventual consistency is needed.

    Deduplication Matters at Scale

    With traditional databases, unified or shared database storage, block-level snapshots made image-based recovery and rollback relatively efficient. Distributed storage combined with a scale-out database that commonly involves replication of data rather than joins across tables led to a rapid proliferation of redundant data and increasing storage requirements (three to four copies of every write operation). Effective data reduction at scale is critical to success and economics for effective data protection in this environment.

    Single Points of Failure Do Fail

    Legacy data protection approaches have commonly involved a media server, which presents a choke point for secondary data path and single point of failure. Given the very high ingest rates coupled with the real-time nature of applications deployed on modern scale-out databases, next-generation data protection infrastructure has to be distributed to handle performance (RPO and RTO windows) and resiliency for always-on environments.

    Checklist for Modern Database Data Protection

    • How do you protect your next-generation scale-out databases today? What scenarios of data loss are you worried about?
    • What downtime can your applications or business handle in the event of data corruption?
    • Have you validated backup and recovery for your next-generation databases?
    • Can you prove compliance with regulatory or line-of-business requirements for backup of your critical next-generation infrastructure?
    • Can you recover to a desired or any point in time quickly? What manual effort is required to do this?
    • How much storage is required to store your desired pool of recovery versions?
    • Does your data protection approach scale out with your application and data growth?
    • Can you efficiently refresh your test/dev environment to a smaller or larger cluster size as needed?
    • What happens to your backup solution when a database node fails? Can you still perform backups? How about intermittent network failures?
    • Native formats free you from vendor lock-in and allow for data management services, such as search and analytics on secondary data.
    • Can you recover your data in native format?

    — Jeannie Liou

  • Things to Know When Planning for Cassandra Backup

    Things to Know When Planning for Cassandra Backup

    NoSQL databases such as Apache Cassandra, MongoDB, Amazon DynamoDB, Azure DocumentDB and others are being adopted by enterprises to meet the data requirements of next-generation applications. Specifically, Apache Cassandra database provides linear scalability, a flexible consistency model and shared-nothing architecture that enables enterprises to scale their database footprint according to performance needs.

    As mission-critical applications are being deployed on Cassandra databases, there are enterprise data management requirements also, such as backup and recovery. Today, there are various options available for backup and recovery of Cassandra. These options have pros and cons, which I will elaborate on in this blog post. It is important for a Cassandra database administrator (DBA) to understand the pros and cons of these options before choosing any option and validating products and solutions available to achieve enterprise goals for backup and recovery.

    Let me first point out the important features that DBAs should look for while planning the backup:

    1. Secondary Storage footprint: Efficient storage utilization is key for any backup and recovery design and plan. The backup data set may be much larger than primary setup depending on the retention period of the backup data. Hence, if storage management is not efficient, customer storage capital expenditure could increase multifold, defeating the purpose of data protection.
    2. RPO and RTO: Recovery point objective (RPO) is the maximum targeted period in which data might be lost from an IT service due to a major incident. Recovery time objective (RTO) is the targeted duration of time and a service level within which a business process must be restored after a disaster. Typically, enterprise requirements for these next-generation applications dictate very small RPOs and RTOs.
    3. Backup performance: This is the time spent in backing up the desired data set. Typically, the backup performance should be scalable enough to match the data change rate at the primary cluster. In addition, the time spent in taking the initial full backup of the primary cluster also matters, so periodical backup is not affected.

    Cassandra Backup Options

    Now let’s take a look at four options available for performing Cassandra backup:

    Snapshot-based Backup

    Cassandra provides native node-level snapshot option via the nodetool command. Snapshot creates a hard link to existing Cassandra data files and expects users to delete it after backing up the files. This way, despite changes in Cassandra data, the backup is performed from snapshot files.

    Pros:

    • Simpler to manage: It requires periodical snapshot commands on each of the nodes, triggering backup on snapshot files and removal of snapshot files after backup.

    Cons:

    • Not a true backup: Node-level snapshot doesn’t mean cluster-consistent backup and such a simplistic strategy will result in heavy repairs upon recovery.
    • Potential large RPO: The snapshot operation is not trivial, as it requires flushing of all in-memory data on disk and, hence, frequent invocation of snapshots will impact the production cluster’s performance dearly.
    • Large storage footprint due to data compaction at primary cluster: As part of data compaction, Cassandra merges multiple data files on a node into a new file and, hence, reduces multiple versions of a record to a single and latest version. Compaction not only helps in reduction of data at the primary cluster, it also improves the read performance as it reduces the number of files to look up for a record. As a side effect of compaction, it’s possible that same records will appear in multiple versions, as they are merged into a new compacted file. It isn’t a trivial task to filter out already captured records from a compacted file. Depending on various aspects such as workload type, compaction strategy or versioning interval, compaction may cause multifold more data to be backed up, causing a serious impact on CapEx planning.
    • Snapshot storage management at primary cluster: Cassandra expects users or admins to clean up snapshot files after they are no longer required. It requires a reliable job to clean up the snapshot folders; otherwise, they may affect the primary cluster’s availability due to lack of storage.
    • Siloed solution: Enterprises require multiplatform backup and recovery product and native tools are built specifically for a particular database and don’t scale for multiple databases.

    Incremental Backup

    Cassandra supports incremental backup functionality. If enabled, every time Cassandra flushes memory content as a new file, a hard link for the new file is created in the incremental backup folder. Once the backup of these files is done, the administrator is supposed to delete these hard links. The best part of incremental backup is that the hard link is created only for newly created file due to the flushing of in-memory data contents. Files created due to compaction are not hard-linked.

    Pros:

    • Better storage utilization: There are no duplicate records in the backup, as the compacted files are not backed up.
    • Point-in-time backup: Companies can achieve better RPO, as backing up from the incremental backup folder is a continuous process and, hence, versioning can be done more granularly.

    Cons:

    • Space management at primary cluster: With this option, companies need a reliable space management process on the primary cluster. The incremental backup folder must be emptied after being backed up. Failure to do so may cause serious space issue on the primary cluster.
    • Creates lots of small size file in backup: Since incremental backup facilitates a backup of new files created due to the flush of in-memory data from Cassandra, typically the file size is small. This leads to many small files, making file management and recovery not a trivial task (and fairly expensive compared to the RTO guarantees customers want and expect).

    Incremental Backup in Combination with the Snapshot

    Incremental backup in combination with snapshots is another rudimentary way to take Cassandra point-in-time backups. Periodically, data is backed up from the snapshot, while incremental backup-based files are used for point-in-time needs. This means periodically the incremental backup-based files, which are already covered via snapshot-based backup, must be deleted.

    Pros:

    • Fairly large-size backup files: Only the data between periodical snapshots are from the incremental backup.
    • Point in time: It provides a point-in-time backup and restores.

    Cons:

    • Space management in backup: After every snapshot, the incremental backed-up data needs to be cleaned up. Also, for backed-up files from the snapshot, a large backup storage footprint due to compaction at primary always is a concern.
    • Operationally very heavy: This method requires DBA admins to script solutions; this is not scalable for enterprise scale.

    Commit-Log Backup in Combination with the Snapshot

    This method is similar to incremental backup. Rather than backing up the newly added SSTables, DBA admins archive the commit-log. Periodical snapshot-based backup provides the bulk of backup data, while the archived commit-log is used for the point-in-time backup.

    Pros:

    • Point in time: Snapshot-based backups in the combination of commit-log archival provide a point-in-time backup option.

    Cons:

    • Space management: Again, the commit-log archival space management is admin’s responsibility; this method results in operational heavy solution (OpEx).
    • Restore complexity: The restore is a lot more complex, as part of restore will happen from the commit-log replay.
    • Storage overhead: Snapshot-based backup will have a storage overuse issue because of duplication of data due to compaction. This results in a very large use of secondary storage and, thus, a very high CapEx expenditure.

    Summary

    Overall, Cassandra provides multiple options for backup. Typically, snapshot-based backup in the combination of commit-log archiving/incremental backup is used if the point-in-time restore is a requirement. If point-in-time restore is not a requirement, simple snapshot-based backup is much easier to implement, though it is not a true backup and will result in heavy repairs.

    Most of these options require significant manual effort and are error prone. They work for staging or preproduction and test/dev environments that are smaller in scale and can sustain data loss and high RTO. However, for large enterprise customers and production deployments, there are key requirements such as data reduction via novel compression/de-duplication techniques, various failure handling and faster recovery with desired consistency.

    For mission-critical, always-on applications where minimizing data loss and operational resiliency are important factors,  administrators should consider leveraging next-generation data protection products from companies that specialize in this area.

  • Enterprise Cassandra Deployments

    Enterprise Cassandra Deployments

    Successful Cassandra deployment in the cloud takes a certain amount of skill and knowledge. In this blog I will share best practices for a successful Cassandra deployment in the cloud such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). I will also discuss how you can protect and recover the data in your Cassandra cluster when deployed in the public cloud.

    Cassandra Deployments in the Public Cloud

    There are two main ways to have successful enterprise Cassandra deployments in AWS:

    • Use a ‘managed’ Cassandra deployment. This is a reasonable approach; the only downside is the inability to customize data placement beyond what is typically provided by the vendor.
    • Manage the cluster yourself, if you want to have full control over your Cassandra deployment and customize it to scale and perform according your business needs.

    Please refer documentation from Datastax or Apache Cassandra for detailed best practices for Cassandra deployments. The following best practices are reiterated here to simplify deployment of a scalable Cassandra cluster in AWS.

    Storage

    Both AWS and GCP offer two types of local storage options for their compute instances. For AWS, the options are Elastic Block Storage (EBS) and Ephemeral Storage. In GCP, the options are Persistent Disks and Local SSD. Both options have their own trade-offs. Pick the one that best suits your deployment.

    Persistent Storage

    EBS volumes have performance characteristics similar to network storage such as a NAS or SAN. Because storage from EBS volumes is multitenant in nature, it is not suitable for distributed, log structured databases such as Cassandra. If you want to ensure reliable performance through EBS volumes, use “EBS optimized instances” with “provisioned IOPS.” Since the IO traffic to EBS volumes goes over the instance network, EBS optimized will provide reliable performance, but does not guarantee any increased performance. To get the most performance out of EBS volumes, use instances with multiple disks and separate the volumes used for “Data” and “Commitlog.”

    If you have to use EBS Volumes, go with “EBS Optimized Instances,” with “Provisioned IOPS” and split data and Commit Log on different volumes.

    Ephemeral Storage

    Ephemeral storage is a great option to use for downstream Cassandra environments such as test/dev and staging. Since ephemeral volumes are typically SSDs, there is no need to separate the data and commitlog into separate drives. To optimize performance, choose EC2 instances with multiple ephemeral storage drives and stripe them together into one logical volume. Send data and commitlog to the logical volume. Remember that when you stop the EC2 instance, the data in Ephemeral storage will be deleted.

    Stripe all Ephemeral drives into a single volume to get maximum performance.

    Data Distribution

    Cassandra uses Snitch to determine the topology of the cluster. The Snitch is also used to determine the data center and rack for each node in the cluster. Replication strategy is used to determine the placement of data across the different nodes in the cluster.

    AWS

    1. EC2Snitch works well for single region deployments.
    1. Ec2MultiRegionSnitch works well for cluster distributed across multiple AWS regions. Please note, Multi-DC, Active-Active configuration makes your cluster resilient to region failures and for multi-dc cluster, use “local quorum” to improve performance.

    Google Cloud Platform (GCP)

    1. Use GoogleCloudSnitch for Cassandra deployments in GCP.
    1. GoogleCloudSnitch treats regions as Cassandra data centers and availability zones as racks.

    Other Features

    Both AWS and GCP offer a series of operational capabilities that seem to make it easier to maintain high uptime on Apache Cassandra cluster. These capabilities have to be chosen carefully depending your deployment needs.

    1. Load Balancing: Network-level load balancing service is offered by both AWS (Elastic Load Balancer) and GCP. It is not recommended to put a load balancer in front of Cassandra. It is better to use a client connector that can handle this, consider open source utilities such Presto and Astyanax
    1. Autoscaling: Both AWS and GCP offer Autoscaling facility for compute instances. However, although this functionality looks very enticing, putting all C* nodes in an autoscaling group will not produce the desired result. Instead, create a separate autoscaling group of ‘1 node’ for every node in your cluster. This will ensure that anytime a node goes down, it is automatically brought back up. This will ensure the cluster data placement is not disturbed when nodes go down and nodes coming up do not cause excessive data movement.

    Scalable Backups and Reliable Point-in-Time Recovery

    Backing up your data is a must-have for enterprise organizations. The impact of data loss is so high that no enterprise wants to risk relying alone on replication. Protecting data in a public cloud environment may seem daunting, but there are several options available that can make this easier compared to an on-premise deployment. A simple option may look like one of the native snapshots:

    Storage Snapshots

    I have seen few customers use snapshots of EBS volumes as a way to protect the data in their cluster against logical and/or operational errors. This solution will work but with some severe limitations. On top of the overhead of maintaining the scripts to take snapshots, there is a large overhead in storage costs. Taking a snapshot of volumes does not allow for incremental backups of your data. This causes an exponential bloat in the amount of data stored in snapshots, leading to high cost of the backup solution.

    Amazon S3 or Google Cloud Storage

    Using an S3 bucket as the secondary storage enables enterprises to use low-cost, highthroughput storage, while reducing overall operating costs for storing backup data. Here are a few recommendations when using S3:

    1. S3 Region: Amazon S3 buckets are region-specific. A bucket created in one region will provide better performance characteristics to other infrastructure in the same region. Subsequently, it is important to ensure the S3 bucket used as the secondary storage is in the same region as the production database being protected and the Datos IO cluster.
    1. Storage Class and Lifecycle Policy: Amazon S3 allows users to create lifecycle policies on buckets. These policies dictate the performance and response time characteristics of objects in the bucket. STANDARD_1A is more suitable for longer lived and less frequently access data, and is not suitable as secondary storage. Additionally, do not turn on any lifecycle policies on the buckets used for the secondary storage. All buckets should be in the standard storage class.

    Conclusion

    As enterprises leverage the agility provided by public cloud environments, it’s important to understand the various facets of Cassandra deployments to ensure the efficient use of your money and time.