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One of the coolest parts of my job is talking with customers and partners about their production environment challenges around database technology.  A topic of particular interest lately is in-memory database (IMDB) systems and their integration into an existing environment.

The need for speed
Much of the media coverage of IMDB integrations is heavily focused on speed and loaded with terms like real-time processing, on-demand analytics and memory speed.  But zeroing in on the performance benefits comes at the expense of so many other key aspects of IMDBs. The technology needs to be evaluated as a whole.

Granted, in-memory databases can store data structures in DRAM with latency that is measured in nanoseconds. (Latency of disk-based technology, comparatively, is glacial – clocked in milliseconds.)  Depending on the workload and the vendor’s database engine architecture, DRAM processing can improve database performance by as much as 50X-100X.

How durable is it?
Keep in mind that most relational database systems conform to the ACID (Atomicity, Consistency, Isolation, and Durability) properties of transactions. (You can find a more thorough investigation of these properties in this paper – “The Transaction Concept: Virtues and Limitation” – authored by database pioneer Jim Gray.) The matter of relational database system durability naturally raises the question: But how is data protected from DRAM failures when things go haywire and what is the recovery experience like?  Relational databases implement the durable property to prevent problems associated with power loss or hardware failure to ensure transaction information is permanently captured.

The commonly used WAL (Write Ahead Logging) method ensures that the transaction data is written to a log file (persisted on non-volatile storage) before it is committed and subsequently written to a data file (persisted on non-volatile storage). When the database engine restarts after a failure, it switches to recovery mode to read the log file and determine if the transactions should be rolled forward (committed) or rolled back (cancelled), depending on their state at the time of failure.

Current in-memory database systems support durability and their implementations vary by vendor.  Here is a sampling of durability techniques they use:

  • WAL (Write Ahead Logging)
    • Traditional method described above using a log file.
    • Changes are persisted to non-volatile storage that is used for recovery.
  • Replication
    • Data is copied to more than one location, and can be across different nodes.
    • Recovery can be handled using failover to alternate nodes.
  • Snapshots
    • Database snapshots are taken at intervals.
    • Previous snapshots can be used for recovery.
  • Data Tiering
    • Frequently accessed data resides only in in-memory DRAM structures.
    • Archival or less frequently accessed data resides only on non-volatile storage.
    • Replication can be used as well.

Shopping tip: Consider durability when evaluating your options
If changes in your data environment are frequent and require greater persistence and consistency, be sure to also consider durability when evaluating and comparing vendor implementations.  Durability is no less important than query speed.  Different implementations may or may not be a good fit and in some cases might require additional hardware that can increase cost.

It’s easy to get swept away by all the media attention about how in-memory databases deliver blazing performance, but customers often tell me they would gladly give up some performance for rock-solid stability and interoperability.

For our part, LSI enterprise PCIe® flash storage solutions not only perform well but also include DuraClass™ technology, which can increase the endurance, reliability and power efficiency of non-volatile storage used for in-memory database systems.

*Old suitcase by allesok used with permission.

 

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Customer dilemma: I just purchased PCIe® flash cards to increase performance of my enterprise applications that run on Linux® and Unix®. How do I set them up to get the best performance?

Good question. I wish there were a simple answer but each environment is different. There is no cookie-cutter configuration that fits all, though a few questions will reveal how the PCIe flash cards should be configured for optimum performance.

Most of the popular relational and non-relational databases run on many different operating systems. I will be describing Linux-specific configurations, but most of them should also work with Unix systems that are supported by the PCIe flash card vendor. I’m a database guy, but these same principals and techniques that I’ll be covering apply to other applications like mail servers, web servers, application servers and, of course, databases.

Aligning PCIe flash devices
The most important step to perform on each PCIe flash card is to create a partition that is aligned on a specific boundary (such as 4k or 8k) so each read and write to the flash device will require only one physical input/output (IO) operation. If the card is not partitioned on such a boundary, then reads and writes will span the sector groups, which doubles the IO latency for each read or write request.

To align a partition, I use the sfdisk command to start a partition on a 1M boundary (sector 2048). Aligning to a 1M boundary resolves the dependency to align to a 4k, 8k, or even a 64k boundary. But before I do this, I need to know how I am going to use this device. Will this be a standalone partition? Part of a logical volume? Or part of a RAID group?

Which one is best?
If I were deploying the PCIe flash device for database caching (for example, the Oracle database has provided this caching functionality for years using the Database Smart Flash Cache feature, and Facebook created the open source Flashcache used in MySQL databases), I would use a single-partitioned PCIe flash card if I knew the capacity would meet my needs now and over the next 5 years. If I selected this configuration, the sfdisk command to create the partition would be:

echo “2048,,” | sfdisk –uS /dev/sdX –force

This single partitioning is also required with the Oracle® Automatic Storage Management system (ASM). Oracle has provided ASM for many years and I will go over how to use this storage feature in Part 3 of this series.

If I need to deploy multiple PCIe flash cards for database caching, I would create Logical Volume Manager (LVM) over all the flash devices to simplify administration. The sfdisk command to create a partition for each PCIe flash card would be:

echo “2048,,8e” | sfdisk –uS /dev/sdX –force

“8e” is the system partition type for creating a logical volume.

Neither of these solutions needs fault tolerance since they will be used for write-thru caching. My recent blog “How to optimize PCIe flash cards – a new approach to creating logical volumes” covers this process in detail.

If I want to use the PCIe flash card for persisting data, I would need to make the PCIe flash cards fault tolerant, using two or more cards to build the RAID array and eliminate any single point of failure. There are a number of ways to create a RAID over multiple PCIe flash cards, two of which are:

  • Use LVM with the RAID option.
  • Use the software RAID utility MDADM (multiple device administration) to create the RAID array.

But what type of RAID setup is best to use?
Oracle coined the term S.A.M.E. – Stripe And Mirror Everything – in 1999 and popularized the practice, which many database administrators (DBA) and storage administrators have followed ever since. I follow this practice and suggest you do the same.

First, you need to determine how these cards will be accessed:

  • Small random reads and writes
  • Larger sequential reads
  • Hybrid (mix of both)

In database deployments, your choice is usually among online transaction processing (OLTP) applications like airline and hotel reservation systems and corporate financial or enterprise resource planning (ERP) applications, or data warehouse/data mining/data analytics applications, or a mix of both environments. OLTP applications involve small random reads and writes as well as many sequential writes for log files. Data warehouse/data mining/data analytics applications involve mostly large sequential reads with very few sequential log writes.

Before setting up one or many PCIe flash cards in a RAID array either using LVM on RAID or creating a RAID array using MDADM, you need to know the access pattern of the IO, capacity requirements and budget. These requirements will dictate which RAID level will work best for your environment and fit your budget.

I would pick either a RAID 1/RAID 10 configuration (mirroring without striping, or striping and mirroring respectively), or RAID 5 (striping with parity). RAID 1/RAID 10 costs more but delivers the best performance, whereas RAID 5 costs less but imposes a significant write penalty.

Optimizing OLTP application performance
To optimize performance of an OLTP application, I would implement either a RAID 1 or RAID 10 array. If I were budget constrained, or implementing a data warehouse application, I would use a RAID 5 array. Normally a RAID 5 array will produce a higher throughput (megabits per second) appropriate for a data warehouse/data mining application.

In a nutshell, knowing how to tune the configuration to the application is key to reaping the best performance.

For either RAID array, you need to create an aligned partition using sfdisk:

echo “2048,,fd” | sfdisk –uS /dev/sdX –force

“fd” is the system identifier for a Linux RAID auto device.

Keep in mind that it is not mandatory to create a partition for LVMs or RAID arrays. Instead, you can assign RAW devices. It’s important to remember to align the sectors if combining RAW and partitioned devices or just creating a basic partition. It’s sound practice to always create an aligned partition when using PCIe flash cards.

At this point, aligned partitions have been created and are now ready to be used in LVMs or RAID arrays. Instructions for creating these are on the web or in Linux/Unix reference manuals. Here are a couple of websites that go over the process of creating LVM, RAID, or LVM on RAID:

https://raid.wiki.kernel.org/index.php/Partitioning_RAID_/_LVM_on_RAID
http://www.gagme.com/greg/linux/raid-lvm.php

Specifying a stripe width value
Also remember that, when creating LVMs with striping or RAID arrays, you’ll need to specify a stripe width value. Many years ago, Oracle and EMC conducted a number studies on this and concluded that a 1M stripe width performed the best as long as the database IO request was equal to or less than 1M. When implementing Oracle ASM, Oracle’s standard is to use 1M allocation units, which matches its coarse striping size of 1M.

Part 2 of this series will describe how to create RAW devices or file systems.

Part 3 of this series will describe how to use Oracle ASM when deploying PCIe flash cards.

Part 4 of this series will describe how to persist assignment to dynamically changing NWD/NMR devices.

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The lifeblood of any online retailer is the speed of its IT infrastructure. Shoppers aren’t infinitely patient. Sluggish infrastructure performance can make shoppers wait precious seconds longer than they can stand, sending them fleeing to other sites for a faster purchase. Our federal government’s halting rollout of the Health Insurance Marketplace website is a glaring example of what can happen when IT infrastructure isn’t solid. A few bad user experiences that go viral can be damaging enough. Tens of thousands can be crippling.  

In hyperscale datacenters, any number of problems including network issues, insufficient scaling and inconsistent management can undermine end users’ experience. But one that hits home for me is the impact of slow storage on the performance of databases, where the data sits. With the database at the heart of all those online transactions, retailers can ill afford to have their tier of database servers operating at anything less than peak performance.

Slow storage undermines database performance
Typically, Web 2.0 and e-commerce companies run relational databases (RDBs) on these massive server-centric infrastructures. (Take a look at my blog last week to get a feel for the size of these hyperscale datacenter infrastructures). If you are running that many servers to support millions of users, you are likely using some kind of open-sourced RDB such as MySQL or other variations. Keep in mind that Oracle 11gR2 likely retails around $30K per core but MSQL is free. But the performance of both, and most other relational databases, suffer immensely when transactions are retrieving data from storage (or disk). You can only throw so much RAM and CPU power at the performance problem … sooner rather than later you have to deal with slow storage.

Almost everyone in industry – Web 2.0, cloud, hyperscale and other providers of massive database infrastructures – is lining up to solve this problem the best way they can. How? By deploying flash as the sole storage for database servers and applications. But is low-latency flash enough? For sheer performance it beats rotational disk hands down. But … even flash storage has its limitations, most notably when you are trying to drive ultra-low latencies for write IOs. Most IO accesses by RDBs, which do the transactional processing, are a mix or read/writes to the storage. Specifically, the mix is 70%/30% reads/writes. These are also typically low q-depth accesses (less than 4). It is those writes that can really slow things down.

PCIe flash reduces write latencies
The good news is that the right PCIe flash technology in the mix can solve the slowdowns. Some interesting PCIe flash technologies designed to tackle this latency problem are on display at AIS this week. DRAM and in particular NVDRAM are being deployed as a tier in front of flash to really tackle those nasty write latencies.

Among other demos, we’re showing how a Nytro™ 6000 series PCIe flash card helps solve the MySQL database performance issues. The typical response time for a small data read (this is what the database will see for a Database IO) from an HDD is 5ms. Flash-based devices such as the Nytro WarpDrive® card can complete the same read in less than 50μs on average during testing, an improvement of several orders-of-magnitude in response time. This response time translates to getting much higher transactions out of the same infrastructure – but with less space (flash is denser) and a lot less power (flash consumes a lot lower power than HDDs).

We’re also showing the Nytro 7000 series PCIe flash cards. They reach even lower write latencies than the 6000 series and very low q-depths.  The 7000 series cards also provide DRAM buffering while maintaining data-integrity even in the event of a power loss.

For online retailers and other businesses, higher database speeds mean more than just faster transactions. They can help keep those cash registers ringing.

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