Setting user Postgres passwords via MD5

Say you want to create a Postgres account for a user but you don’t want them to have to reset it after logging in, and you don’t want to do the “come type in a password on my computer” routine. Well, here’s one way around this.

  1. Have the user generate an MD5 of their password on their local computer. Postgres uses the username as the salt for the hash, so the command to generate the md5 on a Mac would be (assuming a username of ‘ehoffman’ and a password of ‘abcdefg’):
    [evan@Evans-MacBook-Pro ~] $ echo -n abcdefgehoffman | md5

    The “-n” is important – without it, the newline gets included in the hash. After they generate their MD5, have them send it to you (along with their username).

  2. Create the user’s account in the database (or ALTER ROLE if it already exists):
    CREATE ROLE ehoffman LOGIN INHERIT ENCRYPTED PASSWORD 'md595eebfcce27162773a3828689df9d79e';

    Syntax for an existing account:

    ALTER ROLE ehoffman ENCRYPTED PASSWORD 'md595eebfcce27162773a3828689df9d79e';

That’s it. This has the added benefit of the password never being logged in the DB logs or the .psql_history. The main downside is the possibility of user error.

The not-so-secret secret to Postgres performance

I manage a bunch of Postgres DBs and one of the things I almost always forget to do when setting up a new one is set the readahead up from the default of 256. I created this script and run it out of /etc/rc.local and sometimes cron it too. The 3 commands at the top are only really relevant on systems with “huge” memory – probably over 64 GB. We ran into some memory problems with CentOS 6 on a box with 128 GB ram which we ended up working around by reinstalling CentOS 5, but the /sys/kernel/mm/redhat_transparent_hugepage/ options below should fix them in 6.x (though we haven’t actually tried it on that DB, we haven’t seen any problems in other large DBs).


echo no > /sys/kernel/mm/redhat_transparent_hugepage/khugepaged/defrag
echo never >/sys/kernel/mm/redhat_transparent_hugepage/defrag

echo 1 > /proc/sys/vm/dirty_background_ratio

BLOCK_DEVICES=`perl -ne 'chomp; my @a=split(/[s]+/); next unless $a[4]; next if ($a[4] =~ /sd[a-z]+[d]+/); print "$a[4]n";' /proc/partitions `

logger -t tune_blockdevs "Block devices matching: $BLOCK_DEVICES"

for DEV in $BLOCK_DEVICES;  do
        logger -t tune_blockdevs "Setting IO params for $DEV"
### Uncomment the below line if using SSD
#        echo "noop" > /sys/block/$DEV/queue/scheduler
        /sbin/blockdev --setra 65536 /dev/$DEV

Goodbye, pg_dump

I’ve been a Postgres user and administrator for a while. Over the years, my views on backups have evolved.

Originally, like most people, I started out with good old pg_dump. With a reasonably small database (under 50 GB) dumping to a flat text file is a fine option. I’d generally do something like pg_dump -Upostgres dbname | gzip > dbname.sql.gz to compress it on the fly and save space. For years this seemed perfect: dumping the entire database in a single transaction into a single file that can be restored anywhere.

But as my databases started growing larger and larger, the time it took to do a pg_dump grew as well. At a previous job, the database grew to nearly 2TB and the pg_dump took nearly 18 hours. We’d by that point already changed the pg_dump schedule from daily to weekly and then to three times a month and then finally to semi-monthly. Not only was it slow, but since it operated in a single transaction it wreaked havoc with normal database operation for queries that needed locks on tables locked by the dump.

When we moved the database from a physical RAID to a volume on our SAN, that gave us the opportunity to use LUN snapshotting rather than pg_dump (I just remembered I already wrote about that here). This let us move to a monthly pg_dump and more frequent snapshot-level backups that took up very little space. This was ideal on Compellent since the snapshots would auto-expire after however long you specified.

When I started at Yodle we were doing nightly pg_dumps and pretty soon we ran into the same problems I’d seen at Didit with the dump itself interfering with normal DB operation – the dump would start at midnight and run until 7-8 AM when I started, and after a few months it would still be running at noon. We discussed moving to wal archiving and making a basebackup to NFS but that would require a pretty massive amount of space, and as anybody who uses “enterprise storage” knows, that’s not something you want to do. We discussed building a whitebox file server for backups but nobody was really in love with that option – we’re trying to reduce the reliance on physical machines as much as possible. We talked about pushing it all to S3 but that seemed rather difficult.

When I attended NYC PgDay earlier this year, there was lots of discussion about WAL-E. I hadn’t ever head of WAL-E so I looked it up and was impressed. Basically, WAL-E handles archiving of wal to S3, but first compresses and pgp-encrypts it. It also handles pushing the basebackup to S3, also compressed and pgp-encrypted. This was just what we were looking for. We set it up and, amazingly, it worked perfectly. After a few weeks (and confirming we can restore from the wal-e backups) we moved our pg_dump to weekly, on the weekend when it doesn’t interfere with any user processes. We do a wal-e basebackup every 3-4 days or so and retain 3 of them. We retain all the wal so we can restore the DB to any point within the last ~10 days if needed. The best part is it’s faster than pg_dump, and since the basebackup doesn’t operate in a transaction (it’s a filesystem-level backup rather than an application-level backup) it doesn’t mess with user queries. There’s of course elevated IO during this time but our SAN has more than enough bandwidth.

We setup some basic monitoring of S3 (check the age of the most recent WAL and log it in Zabbix) just to ensure the backups are actually happening, and we’re at the point where we’re discussing moving pg_dump to monthly, or simply not doing it at all. Overall, wal-e has been a huge win for us, enabling better, faster backups that don’t interfere with the DB itself, and, while not free, aren’t ridiculously expensive. And since it’s in its own S3 bucket, you can tweak the bucket settings (e.g. enable RRS) to save money, and Amazon tells you exactly how much your backups cost you.

Problems with PostgreSQL 9.0 on CentOS 6.3 with a 2.6.32-series kernel

Note: this is cross-posted from the Yodle Tech Blog.

We recently migrated our Postgres database to a significantly more powerful machine. While our existing server was doing okay load-wise, we were approaching 90% full on its Postgres volume. We’d also recently acquired an awesome new PureStorage SSD-based SAN and were excited to move our database onto it. For comparison, here are the the specs for the two systems:

Old server New server
Nickname DB1 DB2
CPU 2x 6-core Xeon X5690 CPUs at 3.47GHz 2x 8-core E5-2690 CPUs at 2.90 GHz
Memory 96 GB 1333 MHz 128 GB 1600 MHz
  • Dell PERC H800 SAS HBA
  • Dell MD1220 drive enclosure
  • 24x 15k RPM SAS drives configured in RAID 10
  • QLogic QLE2562 dual-port 8 Gbit FC HBA
  • PureStorage SSD-based SAN
OS CentOS 5.6 x86_64 CentOS 6.3 x86_64
Kernel 2.6.18-238.9.1.el5 2.6.32-279.2.1.el6.x86_64

As you can probably guess, moving from spinning disk onto SSD was the centerpiece of the upgrade. We’d tested the PureStorage system pretty extensively and used it in some secondary databases and seen some amazing improvements. We were excited to see how it would handle our production workload.

Basic Configuration

DB1 was built in early 2011 and was running CentOS 5.6. We’re generally pretty big fans of CentOS, so when building DB2 we went with CentOS 6.3, which was the latest version. We followed our standard installation procedure, which essentially means doing a minimal installation and then installing just what we needed, and of course updating all packages to the latest versions.

We provisioned a big LUN for the Postgres data and 500 GB each for Postgres “temp” and logging. While a separate partition/volume for pg_log/pg_xlog is a common practice, we also created a separate volume for temp as a safeguard against runaway queries that generate lots of temp files due to hash-joins or sorting gigantic result sets. In the past we’ve run into situations where the temporary files consume all available space, and if the sort space is on the same partition as your data you run the risk of crashing the database if Postgres attempts to allocate space and there’s none to be had. Since all the SAN volumes are thin provisioned, there’s no penalty for creating separate volumes, and we get the additional benefit of being able to specify different mount options for each volume. (It’s worth noting that PostgreSQL 9.2 allows you to specify a maximum size for temp space, but since we’re still on 9.0 that option isn’t available to us.) We then created XFS filesystems on each volume. We didn’t bother partitioning the volumes, just created the filesystems on the raw devices. This way, in the event we need to expand a volume, we don’t have to worry about growing the partition, just the LUN (in the SAN) and the filesystem.

The initial cutover

We configured streaming replication between DB1 and DB2 to get DB2 in sync with DB1. On the night of Sunday, August 19th, we cut over from DB1 to DB2 as the primary. All things considered, everything went well and our database was only inaccessible to our servers for a few minutes. (Incidentally, while the database was “down” all our client sites were fine since they are completely decoupled from the database.) After verifying everything was up we ran some test queries and confirmed the performance was awesome, gave ourselves some virtual high-fives and went to bed.

The next day, Monday, started out well. Everything that touched the DB was much faster. Pages that took 3-5 seconds to load were nearly instant. Jobs that took hours to run finished in under 15 minutes. It was as if someone had hit the turbo button on our whole system. Everything was wonderful. Or so we thought.

The problems begin

Around midday Monday we started seeing lots of errors being thrown in our applications with the cause of “too many clients” to the database. Our connection limit has been set at 250 forever, so it seemed strange to encounter problems with the connection limit out of the blue. We had recently done a code release so our first line of thought was that there was a bug in the code. We use Hibernate, and it’s pretty easy to blame Hibernate. The problem was intermittent — every 20-30 minutes we’d see a bunch of SQLExceptions due to “too many connections”, which would last 1-2 minutes, and then it would clear up and everything was fine again. We made it through the rest of the day, and in the evening the problem seemed to have cleared up. We thought maybe it was a transient problem that had resolved itself but we saw a bunch of the same exceptions in some jobs that ran overnight. Our system is pretty resilient though, and most connection attempts automatically retried until they succeeded. The problem was a high priority but since it wasn’t affecting end users we were able to limp along. Tuesday was much the same as Monday and we continued investigating. Wednesday the real trouble started.

The real trouble starts

Our nightly pg_dump, which would normally finish by 8:30 AM, was only halfway complete by 8 AM on Wednesday. We had the same too-many-clients exceptions throughout the night. But around midday we started getting reports from users that our UI was really slow: some pages that had been loading nearly instantly were taking 30-60 seconds to load, or more. This quickly turned the problem into a crisis. We saw the same “too many connections” errors in the frontend logs as we had in the backend. The basic behavior was the same – we’d get a wave of “too many connections” for a couple minutes, then it would clear up for a while, and then the cycle would repeat.

We finally had to admit that maybe the problem wasn’t in the code, but in the database. To mitigate the error, we lowered the active connection limits in our connection pools, which improved the “too many connections” error for users, but led page loads to hang while the connection pool waited for a slot to open. This wasn’t much of an improvement for end users though – rather than failing quickly, their page loads were hanging indefinitely.

A minor breakthrough – and a workaround

Thursday morning we were able to get much closer to the actual problem by running vmstat on the database itself. vmstat shows the amount of memory cached vs free along with IO in/out. As people who’ve run Postgres databases before are probably aware, it’s common to see almost 100% of available memory “used” in top, even when the database doesn’t seem to be doing very much. This is due to caching.

What we saw in vmstat, however, was that during the “problem” periods the “cached” was decreasing while the “free” was increasing while the io in/out dropped to nearly zero. After some thought, we realized that the reason for the “too many connections” errors was that queries that normally finished in a few milliseconds were instead taking 30+ seconds due to the IO blocking we were seeing in vmstat. With queries running much longer than usual, they quickly ran over each other and we hit the limit of 250.

So the root behavior we were seeing that seemed to be causing the problem was:

  • Periodically, something was aggressively attempting to reclaim cached memory.
  • While memory was being reclaimed, IO was almost completely blocked.
  • While IO was blocked, database queries naturally took longer than usual, leading to rapid consumption of available database connections.

Now that we seemed to have a root cause (or more concrete symptom) we were able to formulate some relevant search queries. Simply Googling for linux memory reclaim, one of the results on the first page was from the PostgreSQL performance list and seemed to describe very similar behavior on machines with a lot of cores. DB2 has 16 physical cores, and we had logical cores enabled, for a total of 32. Maybe that’s “a lot.” We found some other similar threads and even this blog post written on the very same day we started Googling (how’s that for coincidence?). We also found references to Linux & NUMA memory-reclaim problems for MySQL and MongoDB. Most posts seemed to indicate there’s a problem in the 2.6.32 kernel line with optimizations related to NUMA memory. If experiencing problems, the suggested remedy was to set /proc/sys/vm/zone_reclaim_mode to 0 if using NUMA. Well, /proc/sys/vm/zone_reclaim_mode was already zero, and we weren’t using NUMA anyway – we were using interleaved memory. But this was enough to plant the seed of doubt in our minds for the 2.6.32 kernel. DB1 had been running for well over a year without issue on a 2.6.18 kernel.

On Thursday, thankfully, we discovered that “manually” clearing the cache caused the IO blockage to clear up immediately, at least until the system had time to fill it back up again and the memory-reclaim process kicked in again. The cache is dropped by executing echo 1 > /proc/sys/vm/drop_caches. Every time queries began to block, dropping the cache immediately cleared it up. Since we now had a stopgap workaround, we just cronned dropping of the cache for every 30 minutes. This let us get through Friday and the weekend without having to babysit the database too much for failed connections, etc.

On Friday we decided the best course of action was to put a QLogic fibre channel HBA in DB1 (with its presumably known-good 2.6.18-series kernel), provision some LUNs on it, set it up as a streaming replica and fail back over to it. This way we’d be on a good server but still have the IO benefits of the SSD. We ordered the card from Newegg Friday and received it Monday.

Tactical Retreat

I installed the card Monday (8/27), configured DB1 as a replica, tested it Tuesday, and Wednesday evening we cut back over to DB1 as our primary. With fingers crossed, we went into Thursday and thankfully everything was fine. While overall performance was a little slower than it had been on DB2 (before it had gotten really bad), it was still blazingly fast compared to how it had been on spinning disk. But most importantly there was no evidence of memory-reclaim problems.

We set DB2 up as a hotstandby and performed some testing on it to try and recreate the problem. Unfortunately we didn’t have a lot of success (possibly due to hostandbys being read-only replicas of the primary) but based on the grumbling about NUMA and memory-reclaim in the 2.6.32 kernel line, we felt the best course of action was to go to an older, proven kernel. Since DB1 seemed to be fine with its 2.6.18-238.9.1.el5 kernel, we opted to do a clean install of CentOS 5.8, running the latest in the CentOS 2.6.18 line: 2.6.18-308.13.1.el5.

DB2 reborn

We installed CentOS 5.8, remounted the existing LUNs, resynced it with the primary and did some more testing. We didn’t notice any problems, but since we weren’t really able to reproduce the problem reliably in the first place that wasn’t much consolation. On Thursday, 9/6, we cut back over to DB2. As I write this, DB2 has been in service for several weeks without any sign of the memory reclamation issue, or, more importantly, the IO/query blocking.


While we’re really happy to have (apparently) resolved the problem, we found the resolution somewhat unsatisfying. We had tested DB2 pretty extensively internally prior to putting it into production the first time, literally running millions of real-world queries against it and never encountered anything like the blocking we saw once it took on the production workload. Prior to making it our primary DB we had used it for several days as a hot-standby read-only reporting server and still never saw an issue. After we cut back from DB2 to DB1 we did yet more testing and still weren’t able to reproduce the query-blocking behavior. While this definitely felt like a problem related to the Linux kernel we didn’t have any hard evidence, and going back to a 2.6.18-series kernel was to some degree a leap of faith. Thankfully, so far it’s worked out for us.

Though we didn’t ultimately figure out what it was in the kernel that caused the problem, we did learn a lot from the experience. Most importantly, we ironed out our database cutover procedure to the point where it’s not really a major event to cutover. Since clearly at some point we’ll want to upgrade from CentOS 5.8, we know that we can always fail back to another database with minimal interruption if a problem like this arises in the future.

Graphing SSH dictionary attacks with HighCharts

After my 10-year-old basement Linux server died this week from a power outage, I took the sad step of giving up on it. It’s died before and I’ve patched it back together with a new power supply here or an addon PCI SATA card there, but I finally decided to throw in the towel since I had a newer old computer that had been idle for several years. The one that died was an Athlon K7 750 MHz with 512 MB ram. The new one is an Athlon 2 GHz (3200+) with 1 gig. For my uses, specs don’t really matter that much, but it’s nice to have more power for free.

I put CentOS 6 on it and configured Samba and copied all the data off the old machine and was back up and running within a few hours. Since I forward ports through my FiOS router to this box I did my standard lockdown procedure, including adding myself to the AllowUsers in sshd_config. Afterwards I took a look in /var/log/secure and saw the typical flood of dictionary attacks trying to get in as root or bob or tfeldman or jweisz. I have iptables configured to rate-limit SSH connections to 2 per 5 seconds per IP so the box doesn’t get DoSed out of existence, but some stuff does make it through to sshd.

Looking through /var/log/secure, I got to thinking it would be interesting if there was some way to visualize the attacks in a handy graph. Then I remembered, oh, wait, I can do that.

I wrote a perl script to parse out the attacks from /var/log/secure and insert them into a Postgres DB. This turned out to be pretty easy. Then I thought it would be more interesting to tie the IP of each attack to its originating country. I’ve used MaxMind’s GeoIP DB pretty extensively before, but I was looking something free. That’s when I remembered that MaxMind has a free GeoIP DB: GeoLiteCity. I grabbed it and yum-installed the Perl lib and added the geo data to the attack DB. Rather than worry about normalizing the schema I just shoved the info into the same table. Life is easier this way, and it’s just a for-fun project.

So I got that all working and parsed it against the existing /var/log/secures via

[root@lunix2011 ~]# zcat /var/log/secure-20111117.gz | perl 

I wrote ssh.php to see what’s in the table:

ssh.php list of hacking attempts
ssh.php list of hacking attempts

So now that the data was all in place, time to move on to the graphs, which is what I really wanted to do. Last time I wanted to graph data programmatically I used JPGraph, which does everything in PHP and is super versatile. But I wanted something… cooler. Maybe something interactive. A little Googling turned up Highcharts which is absolutely awesome, and does everything in JavaScript. I basically modified some of their example charts and pumped my data into them and got the charts below.

Pie chart of attacks grouped by country for the past 30 days:

Pie chart by country
Pie chart by country

Bar graph of attacks per day:

Bar graph of daily attacks
Bar graph of daily attacks

So, that’s that. Code is in github if anyone wants to play around with it. I’ve cronned to run every 5 minutes so the data gets updated automatically.

Using WAL archiving & Compellent snapshots for PostgreSQL backups

I seem to have what may be an irrational dislike for differential backups in general. No matter what system I’m backing up, I feel far more confident in doing complete backups than differential ones. The significant exception to this is rsync, but even rsync does “full” backups of the files that have changed. Even so, I usually add the -W flag to rsync so it moves the entire file if it’s been modified. I guess I just like knowing there’s a single file that contains the entire database rather than having to restore a huge file and then replay a bunch of differential files in sequence.

This has worked for a long time, even with our PostgreSQL DB, which I’ve been backing up with good ol’ pg_dump, but it’s gotten to the point that the DB backup takes over 12 hours now, during which performance is seriously degraded. With the recent migration to the new server, overall performance seems significantly better overall, but performance during the nightly pg_dump is much worse. Rather than trying to troubleshoot it, I think it’s time I bit the bullet and move to WAL archiving to enable differential backups, and stop doing a full backup every night.

The PostgreSQL docs are pretty great at explaining how to do this. Basically:

  1. Configure WAL archiving to copy the WAL files to another server as soon as they’re complete.
  2. Issue the pg_start_backup() command.
  3. Perform a filesystem-level backup of the DB (usually /var/lib/pgsql on RedHat/CentOS distros).
  4. Issue the pg_stop_backup() command.

That’s basically it. The backup from step 2 plus the archived WAL files will allow you to recover to any point in time after issuing the pg_stop_backup() command. So if you complete your backup at 2 AM on Sunday and your DB crashes on Wednesday at 6 PM, you can restore to any point between 2 AM Sunday and whatever your most recently archived WAL file is (presumably within a few minutes of the crash at 6 PM Wednesday). If you decided you wanted to restore to 12:01 AM on Wednesday, you can do that using the recovery_target_time setting. My aversion to differential backups aside, this is pretty awesome.

As I said, the Postgres docs do a good job of covering this procedure completely. The only thing I have to add is how I did this using a SAN-level snapshot as my “filesystem backup.” I’d never done any scripting with Compellent before and it turned out to be pretty easy. From the Knowledge Center, under Software download the latest version of Command Utility (I downloaded 5.4.1). The utility itself is just a JAR, you’ll need Java installed to run it.

I considered doing a real filesystem backup of the DB (love that rsync) but the problem was that the DB is currently 1.2 TB and gobbling that much space on the NAS wasn’t very appealing. Compellent replays (snapshots) are just deltas, and I can easily store a week worth of backups for much less than (7 * 1.2 = ) 8.4 TB.

I wrote a crappy bash script to do everything, included below:

I retain the WAL files for 8 days and the snapshots for 7 days, but I may adjust this since the WAL files themselves consume a lot of space – about 30-40 GB per day. Though this is still less than the gzipped pg_dump I had been doing, which was about 85 GB per day.

I’ve cronned the script to run at 2 AM and so far it appears to work. Compellent replays are created almost instantly, so the backup script completed in about 10 seconds, which includes 6 seconds of sleep, which probably aren’t necessary. Considering that the pg_dump method took 12+ hours to complete, 10 seconds is immeasurably better. Well, I guess it is measurable, you just need to divide 12 hours by 10 seconds.

I’m pretty happy with this so far. The improved performance far outweighs any irrational philosophical objection I may have had to differential backups. Buuuut I’m still going to do pg_dumps on Saturdays.