Venu Anuganti Blog

Recently I came across this new Massachusetts state data protection security law that has been passed and wondering if anyone took an initiative to fix their data storage, especially if it deals with MA residents. You can find more about this law from Google Search.

One thing that might make a difference for database vendors and users are storing personal information without any encryption and replicating that across the wire and also needs to maintain a Written Information Security Plan (WISP) and file it with the state of Massachusetts.

The main problem is; if you have 1000 users from MA; and if you did not encrypt their personal identification information (PII); then you or your business might end up paying 5M USD (5K per breach or lost record); and same is the case when you loose the data that is stored in USB or laptop or whatever…

This also means; if other states and countries start implementing the same rules; then we might see traction on how the databases actually store the data by having global encryption at different levels like table, file, database or at a system level. Microsoft SQL server 2008,  already started supporting encryption at various levels by introducing Transaparent Data Encryption (TDE)

In the last post choosing about the right type; there is a case about quoting the tuple values; that I forgot to mention which is pretty much a common mistake when string data types are used for storing int or float/double representation (well sometimes you need to use string due to length or to avoid precision loss); and queries associated with that column does not quote the data to be string when searching…

In the same example; client_id was declared as VARCHAR(255); so without any quotes searching on client_id takes 11 secs:

mysql> explain SELECT SQL_NO_CACHE channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = 1301 GROUP BY client_id, channel;
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+--------------------------+
| id | select_type | table       | type  | possible_keys      | key                | key_len | ref  | rows     | Extra                    |
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+--------------------------+
|  1 | SIMPLE      | xxx_sources | index | idx_client_channel | idx_client_channel | 1032    | NULL | 20207319 | Using where; Using index | 
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+--------------------------+
1 row in set (0.00 sec)
 
mysql> SELECT SQL_NO_CACHE channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = 1301 GROUP BY client_id, channel;
+---------+----------+
| channel | visitors |
+---------+----------+
| NULL    |        0 | 
+---------+----------+
1 row in set (11.69 sec)

But if you quote client_id in the search part(client_id=’1301′); then things will run much faster (0.25sec as opposed to 11.69sec) as it does not need to do the conversion, and even the plan uses the direct const checking:

mysql> explain SELECT SQL_NO_CACHE channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = '1301' GROUP BY client_id, channel;
+----+-------------+-------------+------+--------------------+--------------------+---------+-------+--------+--------------------------+
| id | select_type | table       | type | possible_keys      | key                | key_len | ref   | rows   | Extra                    |
+----+-------------+-------------+------+--------------------+--------------------+---------+-------+--------+--------------------------+
|  1 | SIMPLE      | xxx_sources | ref  | idx_client_channel | idx_client_channel | 258     | const | 457184 | Using where; Using index | 
+----+-------------+-------------+------+--------------------+--------------------+---------+-------+--------+--------------------------+
1 row in set (0.00 sec)
 
mysql> SELECT SQL_NO_CACHE channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = '1301' GROUP BY client_id, channel;
+---------+----------+
| channel | visitors |
+---------+----------+
| NULL    |        0 | 
+---------+----------+
1 row in set (0.25 sec)

Same is the case and performance impact if data is quoted when searching on int/double/float columns. At times its worth to double check column data types and use the same notation when using them (with or without quotes)

Today evening one of my friend asked me in the IM to look into one of his production server where a query was taking ~11 seconds to run on 20 million row table, even though the query is using the right index and the plan as shown below:

mysql> explain SELECT channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = 1301 GROUP BY channel;
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+-----------------------------------------------------------+
| id | select_type | table       | type  | possible_keys      | key                | key_len | ref  | rows     | Extra                                                     |
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+-----------------------------------------------------------+
|  1 | SIMPLE      | xxx_sources | index | idx_client_channel | idx_client_channel | 1032    | NULL | 19205420 | Using where; Using index; Using temporary; Using filesort |
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+-----------------------------------------------------------+
1 row in set (0.01 sec)
 
mysql> SELECT channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = 1301 GROUP BY channel;
+---------+----------+
| channel | visitors |
+---------+----------+
| NULL    |        0 |
+---------+----------+
1 row in set (11.61 sec)
 
mysql> show table status like 'xxx_sources'\G
*************************** 1. row ***************************
           Name: xxx_sources
         Engine: InnoDB
        Version: 10
     Row_format: Compact
           Rows: 19882760
 Avg_row_length: 46
    Data_length: 926941184
Max_data_length: 0
   Index_length: 1188233216
      Data_free: 0
 Auto_increment: NULL
    Create_time: 2010-04-15 21:03:37
    Update_time: NULL
     Check_time: NULL
      Collation: latin1_swedish_ci
       Checksum: NULL
 Create_options:
        Comment: InnoDB free: 0 kB
1 row in set (0.21 sec)

Quickly looking at the plan; I added client_id in the group by to avoid temporary table, and the new plan looks much better, but still took same time for execution (well, cost of temp and copy is cheap in this case)..

mysql> explain SELECT channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = 1301 GROUP BY client_id, channel;
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+--------------------------+
| id | select_type | table       | type  | possible_keys      | key                | key_len | ref  | rows     | Extra                    |
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+--------------------------+
|  1 | SIMPLE      | xxx_sources | index | idx_client_channel | idx_client_channel | 1032    | NULL | 19205420 | Using where; Using index |
+----+-------------+-------------+-------+--------------------+--------------------+---------+------+----------+--------------------------+
1 row in set (0.00 sec)

and then examined the data and noticed that client_id was declared as VARCHAR(255) even though the client_id data is all int; quickly changing client_id to int made a big difference as the query execution took only ~0.24 secs

mysql> SELECT channel, COUNT(channel) AS visitors FROM xxx_sources WHERE client_id = 1301 GROUP BY channel;
+---------+----------+
| channel | visitors |
+---------+----------+
| NULL    |        0 |
+---------+----------+
1 row in set (0.24 sec)

The performance difference is too big after changing the type to int. This is just an example; but I noticed lot of tables with VARCHAR(64) or VARCHAR(255) or VARCHAR(512) (or even TEXT at times).. as default types even though they store at max of 10-15 bytes of data; not sure why anyone do that; as this is something that must be followed as rule #1 when designing schema. Even if you are not directly querying on that column; it is always better to design a schema with right type and storage so that it is optimal in terms of storage space and performance.

Ever since MySQL 5.5 beta has been announced by Edward Screven, Oracle’s chief corporate architect; there is lot of positive buzz (here, here, …) about the performance and scalability improvements added in this release. We should all be thankful to Michael Ronstrom (as most of the key developers are already working on different forks), who did a great job in the improvements especially scalability related that allows to scale beyond 16 cores by improving the performance by 2-5X in most common workloads. Not to forget about numerous improvements to replication by replication team.

Even though 5.5 has lot of new improvements officially from Sun/Oracle; but some of the changes are actually driven by community (yet another thanks to Google, Mark Callaghan and his team, Percona and his team, Facebook etc) and most of the ideas or patches were already floating for a while and they were used in the production as well (5.0 or 5.1). This is actually a good sign that community can look forward for 5.5 GA instead of worrying about what patches and builds to use.

This is a clear indication that 5.5 performance and scalability improvements were actually driven by community.

Key improvements in 5.5:

1. InnoDB changes in 1.1
• Multiple buffer pools (controlled by innodb_buffer_pool_instances)
• Multiple rollback segments
• Splitting of purge operation from main background thread (controlled by innodb_purge_threads)
• New log_buf mutex now controls the mini transaction writes in buffer pool instead of shared log_sys, reduces the contention on buffer pool
• Separate mutex for flush list handling, reduces the contention on buffer pool
• Improved recovery time
2. Rest of the changes as part of InnoDB plugin 1.0.x
3. Numerous replication related changes

Even though they announced InnoDB as the default storage engine in 5.5; but the latest build still has MyISAM as the default

Server version: 5.5.4-m3 MySQL Community Server (GPL)
 
Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.
 
mysql> show engines;
+--------------------+---------+----------------------------------------------------------------+--------------+------+------------+
| Engine             | Support | Comment                                                        | Transactions | XA   | Savepoints |
+--------------------+---------+----------------------------------------------------------------+--------------+------+------------+
| InnoDB             | YES     | Supports transactions, row-level locking, and foreign keys     | YES          | YES  | YES        |
| MRG_MYISAM         | YES     | Collection of identical MyISAM tables                          | NO           | NO   | NO         |
| MEMORY             | YES     | Hash based, stored in memory, useful for temporary tables      | NO           | NO   | NO         |
| BLACKHOLE          | YES     | /dev/null storage engine (anything you write to it disappears) | NO           | NO   | NO         |
| CSV                | YES     | CSV storage engine                                             | NO           | NO   | NO         |
| MyISAM             | DEFAULT | Default engine as of MySQL 3.23 with great performance         | NO           | NO   | NO         |
| ARCHIVE            | YES     | Archive storage engine                                         | NO           | NO   | NO         |
| FEDERATED          | NO      | Federated MySQL storage engine                                 | NULL         | NULL | NULL       |
| PERFORMANCE_SCHEMA | YES     | Performance Schema                                             | NO           | NO   | NO         |
+--------------------+---------+----------------------------------------------------------------+--------------+------+------------+
9 rows in set (0.00 sec)

We all know how important hardware RAID controllers are in today’s data storage performance especially when dealing with large data sets. If we look at the trend from now to couple of years back; they really evolved rapidly with lot of useful features and their usage also grown as most of the new servers by default has one or two controllers built-in (one for internal and another one for external storage array or for redundancy).

Few popular RAID controller vendors in the market: 

• PERC Controllers from DELL
• Adaptec RAID Controllers
• 3Ware RAID Controllers (now part of LSI)
• LSI RAID Controllers
• Smart Array Controllers from HP
• Intel RAID Controllers

More or less everyone supports all common features and differs in number of ports, protocol support (ISCSI, SATA, SAS, HBA/FB), transfer speed, RAID levels, total disks support, cache size and its management.

Controller Cache – Database Workloads

For database OLTP workloads (IO bound), controller cache plays a crucial role for overall write or read throughput, depending on how the cache is used. Most RAID controllers are equipped with either 128MB or 256 MB or 512MB cache, and newer controllers like HP Smart Array P812 supports 1GB.

Write-back mode improves the writes performance by magnitude as the write request is returned as completed as soon as the data is in the controller cache without actually writing to the disk (that’s why controller needs a BBU, Battery Backup Module so that there is no data loss on power failures)

In case if you enable the read ahead from the controller (sometimes good for OLAP workloads or ETL data warehouse, especially adaptive read ahead due to heavy sequential access); then the same cache is used to store the pre-fetched data that can be satisfied later from the cache without hitting the disk. But in case if the database system does read ahead (like InnoDB), then it is better to turn off read ahead from controller to avoid page trashing.

For some workloads, the controller cache can also cause negative performance if the cache is not properly utilized by the controller.

Missing Cache Management Tools

At present, none of the controllers either supports any cache management tools nor exposes how the cache has been actually used, so that one can adjust the cache according to the workloads for improved performance.

Some of the missing features:

• A way to flush the data from cache to disks, so that the systems can be taken for offline maintenance. Right now there is no easy way to flush data from cache to disk; other than some of the controllers will indicate through LED whether data is in the cache or not
• Way to set the cache threshold in time or %, so that it can start flushing to disk once it meets the threshold value. For example; if you notice big spikes from RRD graphs for every few minutes, then one can adjust the threshold to evenly distribute the load.
• Cache usage statistics (writes data size, read ahead data size etc ), so that workload can be adjusted to yield much better results
• Splitting of cache between reads and writes either in size or by %; so that they do not overlap and cause performance issues. For example; one prefers to set 20% for read ahead data and 80% for writes. Only HP Smart Array controller supports this feature at present.

As you get more control over the controller cache, the more you can tweak and adjust the workloads to get improved performance. Hopefully one day all vendors will expose more cache management options.

Other day we had a small discussion about data stores and hardware; and which one drives the other when it comes to data storage solution, rather it is a hard discussion as both on its own are bigger entities; and one can not easily conclude as it depends on use cases and actually speaking data store limitation(s) drives the need for more powerful hardware for demanding scalability needs.

We all know how important the hardware is in today’s data scalability, especially when dealing with large data sets. Without hardware, it is hard to scale even if you have a powerful data store either it could be SQL (row or columnar) or NoSQL (key/value or other means) or any other data storage solution; because they are limited by the data structures & its implementation and data store performance directly depends on the hardware lately.

At times, data store vendors claim that they have scalable, high performance architecture; that means the solution is directly built on top of hardware scalability and performance by taking advantage of today’s evolving hardware technology. Also, hardware evolution is too aggressive in the recent years when compared to data store solutions due to the market share as hardware is everywhere as it is not just the storage solution.

In short, when a data store performance is directly proportional to hardware performance; that means the data store actually surpassed all of its software performance bottlenecks (algorithms, decision making, data structures etc). Overcoming from software performance is not that easy as the requirement changes day by day and it depends on data size and how data is actually:

• stored
• retrieved
• processed and
• maintained

If data is stored and retrieved from memory or non-persistent storage solution; then one does not need to worry about rest of the stuff or performance as it yields the best throughput; but memory or non-persistent solution can be a solution for smaller data sets, but not for large data sets that deals with tera bytes of data.

Other than newly evolving columnar data stores (yet to see any one solution that is really pitching with universal acceptance like Oracle/SQLServer/MySQL), NoSQL or big data warehouse solutions (like Aster data, Green Plum etc), none of the existing solutions really take advantage of the latest hardware or even the data  structures as most of the data store kernels are written years back. In today’s world; the only option for scalability is by depending on the hardware and by distributing the load across multiple systems (either in shared-nothing or shared-common or even “cloud” way…).

Hoping to see a solution, one day that actually bridges the gap between data store, hardware and scalability without the need of using multiple technologies for common use cases instead of depending on one single solution that can be universally adopted. Brian Aker in his recent interview and Baron claims the same thought.

Very nice and interesting post from Michael Stonebraker explaining how errors dictate CAP Theorem (Consistency, Availability and Partition-tolerance); as only one objective from the CAP can be achieved during normal error conditions as NoSQL system seems to relax the consistency model as CAP theorem anyway proves that one can’t get all 3 at the same time, by favoring partition based availability

As most NoSQL systems adopt Eventual Consistency (depends and some systems it is configurable, and here is yet another nice article on variations in eventual consistency from Werner Vogels, CTO of Amazon) especially when data is distributed across the cluster of systems. Stonebraker suggests CA (Consistency and Availability, typical SQL system) rather than AP (Availability and Partition-Tolerance, typical NoSQL) by explaining the different error scenarios.

For example (just for fun); let’s consider the same error conditions, one by one and see how this can be adopted in distributed cloud computing (SQL or NoSQL)…

• Application errors;  and up on error, system needs to rollback to its previous consistent state…

In case of transactional system, it is easy to rollback if the unit of work is in transaction (automatically by DBMS or manually by the application). In key/value pair, as easy as calling delete (key). Few systems use append-only mode (where there is no concept of updates or deletes, few SAS companies in the valley also use MySQL/InnoDB/MyISAM in this append only mode where system never gets any deletes/updates as everything runs on INSERT and SELECT only), updating with older time-stamp or serial-number can revert the latest change (depending on how the latest value is read).

• Repeatable system crash on bad query request or bad data or something…

It is really hard to escape from this failure as other system in the cluster or replica can also fail for the same condition unless the replicated system is on a different version (rare case) or other degree of fault tolerance.

• Unrepeatable system crash (System failure, hardware/power issue, data center down, network issue…)

If it is a system failure; then this can be handled by fail-over to another system or replica in the LAN or WAN. NoSQL defines much better solution for fail-over with online substitution of nodes to the cluster. The only issue is, if data is persistent in master node and if it implements read-your own write or monotonic read consistency model (same value upon subsequent requests); and before the change propagates to replica of other nodes; then the consistency model fails unless the model is designed such that more than one node shares the same write.

• Partition failure in LAN, WAN

If bunch of partitions/nodes fail within LAN or WAN due to network, power failure etc; then it is hard to fail-over unless there is a replica or same copy within LAN or WAN. But due to network and/or power redundancy in data center or cloud environment; this failure is very unlikely.

• Fail-over due to slowness or poor response

When one of the node starts performing slow due to higher load or more data processing; instead of failing over to another node (typical NoSQL concept), Stonebraker recommends building a system that can take the load spikes; but it is really hard due to growing data needs and poor capacity planning as things change over time. So, one option is to retire by fail-over to newer one (not always possible if you already have latest and greatest specs) or adding more nodes to take the load spikes (distributed).

But in case of SQL with shared-nothing or shared-common architecture; it is not always possible to distribute the load to more than one system (for example, client X in system A, but when A exceeds the load; only option is to split client X into system A and B; but client X can’t be split due to data integrity or lack of common layer that can interact with both the nodes and perform the join/merge operations)

Both has its advantages and dis-advantages; but if NoSQL adopts strict consistency model as that of SQL; then it is hard to scale in the distributed environment where that architecture is more or less demanded by many big web applications where the scalability comes only by distribution and consistency has to be sacrificed to some extent to get the blend of performance + scalability + high availability

Here is some file IO performance numbers from DELL MD1120 SAS storage array. Last year I did the same test with HP P800 storage array and numbers were impressive. But when it comes to this high end storage array, few surprises.  Before getting into actual details; lets see the test stats and configuration details.

System Configuration:

1. DELL R710 with CentOS 5.4
2. NOOP IO Scheduler
3. MD1120 with 22 10K SAS disks
   • 20 disk RAID-10 (hardware)
   • 2 hot spares
   • Disk Cache disabled
4. PERC 6/E RAID controller with BBU
   • Connected to DELL MD1120 using SAS
   • Write Back
   • Read Cache Disabled

Test Configuration:

1. Sysbench fileio test with variable modes and threads
2. 64 files with 50G total size
3. All tests ran in un-buffered mode (O_DIRECT) as most of the workload is InnoDB based.

Test Results:

Number of Threads vs Number of Requests/Sec. Every mode ran with 5 iterations and average is taken.

Random IO:

Sequential IO:

HDPARM Test:

[test~]# for i in `seq 1 3`; do hdparm --direct -tT /dev/sdc1; done | grep Timing
 Timing O_DIRECT cached reads:   2068 MB in  2.00 seconds = 1033.21 MB/sec
 Timing O_DIRECT disk reads:  2146 MB in  3.00 seconds = 715.32 MB/sec
 Timing O_DIRECT cached reads:   2020 MB in  2.00 seconds = 1010.26 MB/sec
 Timing O_DIRECT disk reads:  2162 MB in  3.00 seconds = 720.62 MB/sec
 Timing O_DIRECT cached reads:   2052 MB in  2.00 seconds = 1025.90 MB/sec
 Timing O_DIRECT disk reads:  2128 MB in  3.00 seconds = 709.17 MB/sec
 
[test ~]# for i in `seq 1 3`; do hdparm -tT /dev/sdc1; done | grep Timing
 Timing cached reads:   18920 MB in  2.00 seconds = 9475.34 MB/sec
 Timing buffered disk reads:  3442 MB in  3.02 seconds = 1141.44 MB/sec
 Timing cached reads:   19332 MB in  2.00 seconds = 9681.56 MB/sec
 Timing buffered disk reads:  3478 MB in  3.00 seconds = 1159.24 MB/sec
 Timing cached reads:   18012 MB in  2.00 seconds = 9019.50 MB/sec
 Timing buffered disk reads:  3492 MB in  3.02 seconds = 1155.53 MB/sec

Analysis:

1. Overall the numbers are not bad when it comes to writes, but few surprises when it comes to reads. When compared with HP’s P800 storage array, the numbers still dropped by 20%.
2. Radon IO:
   • Random write requests ranges from 3200-5000 per sec; due to write back mode (512M cache)
   • Writes are linearly scaling well with the threads, good sign that controller is able to manage the cache efficiently
   • Random reads and writes (rndrw) is also scaling linearly with the threads load, means the IO distribution and cache burst to satisfy reads seems be efficient as it needs to flush the data from controller cache to disk before the read can be satisfied.
3. Sequential IO:
   • Writes seems to be scaling well even in sequential mode without much overhead
   • When it comes to reads, big surprise is drop from 5626 requests/sec to 615 from one thread to two threads. Which is really odd. Worst case it should be ~2000-3000 requests/sec; not sure where the overhead is. I can’t believe it could be thread scheduling as there is only 2 threads.
4. During 100% IO, on and off I noticed IO serialization with higher queue waits, which indicates that there is some degree of serialization overhead in OS; but not able to track which layer is triggering this. Tried with cfq/deadline, still the same.
5. Next attempt will be replacing 3Gb/s SAS to fiber channel HBA or 6Gb/s SAS (PERC H800) to see how it performs along with combination of HW and SW raid instead of only depending on controller.