We have 20.000.000 generated textfiles every year, average size is approx 250 Kb each (35 Kb zipped).
We must put these files in some kind of archive for 10 years. No need to search inside textfiles, but we must be able to find one texfile by searching on 5-10 metadata fields such as "productname", "creationdate", etc.
I'm considering zipping each file and storing them in a SQL Server database with 5-10 searchable (indexed) columns and a varbinary(MAX) column for the zipped file data.
The database will be grow huge over the years; 5-10 Tb. So I think we need to partition data for example by keeping one database per year.
I've been looking into using FILESTREAM in SQL Server for the varbinary column that holds the data, but it seems this is more suitable for blobs > 1 Mb?
Any other suggestions on how to manage such data volumes?
I'd say keeping the files in the filesystem would be a better idea. And you can keep file name and path in the DB. Here's a similar question.
Filestream is definitely more suited to larger blobs (750kB-1MB) as the overhead required to open the external file begins to impact read and write performance vs. vb(max) blob storage for small files. If this is not so much of an issue (ie. reads of blob data after the initial write are infrequent, and the blobs are effectively immutable) then it's definitely an option.
I would probably suggest keeping the files directly in a vb(max) column if you can guarantee they won't get much larger in size, but have this table stored in a seperate filegroup using the TEXTIMAGE_ON option which would allow you to move it to different storage from the rest of the metadata if necessary. Also, make sure to design your schema so the actual storage of blobs can be split over multiple filegroups either using partitions or via some multiple table scheme so you can scale to different disks if necessary in the future.
Keeping the blobs directly associated with the SQL metadata either via Filestream or direct vb(max) storage has many advantages over dealing with filesystem / SQL inconsistencies not limited to ease of backup and other management operations.
I assume by "generated" you mean something like data are being injected into document templates, and so there's much repetition of text content, i.e. "boilerplate" ?
20 million of such "generated" files per year is ~55,000 per day, ~2300 per hour!
I would manage such volume by not generating text files in the first place, and instead by creating database abstracts that contain the data that are pumped into the generated text, so that you can reconstitute the full document if necessary.
If you mean something else by "generated" could you elaborate?
Related
I have a few ultra-wide tables (1500+ columns) which I am trying to load data into. I am loading GZIPped files from S3 using a manifest file.
The distkey of the table is 'date' and each file in S3 contains information for one particular date only. The columns are mostly floats, with a few dates and varchars.
Each file has approximately 16000 rows with 1500 columns, and is approximately 84 MiB gzipped. Even following best practices for loading, we are seeing very poor load performance: 100 records/s or approximately 300 kB/s.
Are there any suggestions for improving load speeds specifically for ultra-wide tables? I'm loading data into narrower tables using similar techniques with fairly reasonable speeds, so I have reason to believe that this is an artifact of the width of the table.
Having files separated by the DISTKEY field does not necessarily improve load speed. Amazon Redshift will use multiple nodes to import files in parallel. The node that reads one particular input file will not necessarily be the same node used to store the data. Therefore, the data will be sent between nodes (which is expected during a load process).
If the table has been newly created, then the load process will automatically use the first 100,000 rows to determine an optimal compression type for each column. It will then delete that data and restart the load process. To avoid this, either create the table with compression defined on each column or run the COPY command with the COMPUPDATE option set to OFF. If, on the other hand, there is already data in the table, then this automatic process will be skipped.
It is possible that the load process is consuming too much memory and is spilling to disk. Try increasing wlm_query_slot_count increase the memory available to the COPY command. However, I'm not sure that this parameter applies to COPY commands (it is for 'queries', and the COPY command might not qualify as a query).
Adding for future reference:
One optimization that helped was switching from Gzipped JSON to CSV files. This reduced each file from 84 MiB to 11 MiB and tripled the loading speed.
We have flat files (CSV) with >200,000,000 rows, which we import into a star schema with 23 dimension tables. The biggest dimension table has 3 million rows. At the moment we run the importing process on a single computer and it takes around 15 hours. As this is too long time, we want to utilize something like 40 computers to do the importing.
My question
How can we efficiently utilize the 40 computers to do the importing. The main worry is that there will be a lot of time spent replicating the dimension tables across all the nodes as they need to be identical on all nodes. This could mean that if we utilized 1000 servers to do the importing in the future, it might actually be slower than utilize a single one, due to the extensive network communication and coordination between the servers.
Does anyone have suggestion?
EDIT:
The following is a simplification of the CSV files:
"avalue";"anothervalue"
"bvalue";"evenanothervalue"
"avalue";"evenanothervalue"
"avalue";"evenanothervalue"
"bvalue";"evenanothervalue"
"avalue";"anothervalue"
After importing, the tables look like this:
dimension_table1
id name
1 "avalue"
2 "bvalue"
dimension_table2
id name
1 "anothervalue"
2 "evenanothervalue"
Fact table
dimension_table1_ID dimension_table2_ID
1 1
2 2
1 2
1 2
2 2
1 1
You could consider using a 64bit hash function to produce a bigint ID for each string, instead of using sequential IDs.
With 64-bit hash codes, you can store 2^(32 - 7) or over 30 million items in your hash table before there is a 0.0031% chance of a collision.
This would allow you to have identical IDs on all nodes, with no communication whatsoever between servers between the 'dispatch' and the 'merge' phases.
You could even increase the number of bits to further lower the chance of collision; only, you would not be able to make the resultant hash fit in a 64bit integer database field.
See:
http://en.wikipedia.org/wiki/Fowler_Noll_Vo_hash
http://code.google.com/p/smhasher/wiki/MurmurHash
http://www.partow.net/programming/hashfunctions/index.html
Loading CSV data into a database is slow because it needs to read, split and validate the data.
So what you should try is this:
Setup a local database on each computer. This will get rid of the network latency.
Load a different part of the data on each computer. Try to give each computer the same chunk. If that isn't easy for some reason, give each computer, say, 10'000 rows. When they are done, give them the next chunk.
Dump the data with the DB tools
Load all dumps into a single DB
Make sure that your loader tool can import data into a table which already contains data. If you can't do this, check your DB documentation for "remote table". A lot of databases allow to make a table from another DB server visible locally.
That allows you to run commands like insert into TABLE (....) select .... from REMOTE_SERVER.TABLE
If you need primary keys (and you should), you will also have the problem to assign PKs during the import into the local DBs. I suggest to add the PKs to the CSV file.
[EDIT] After checking with your edits, here is what you should try:
Write a small program which extract the unique values in the first and second column of the CSV file. That could be a simple script like:
cut -d";" -f1 | sort -u | nawk ' { print FNR";"$0 }'
This is a pretty cheap process (a couple of minutes even for huge files). It gives you ID-value files.
Write a program which reads the new ID-value files, caches them in memory and then reads the huge CSV files and replaces the values with the IDs.
If the ID-value files are too big, just do this step for the small files and load the huge ones into all 40 per-machine DBs.
Split the huge file into 40 chunks and load each of them on each machine.
If you had huge ID-value files, you can use the tables created on each machine to replace all the values that remained.
Use backup/restore or remote tables to merge the results.
Or, even better, keep the data on the 40 machines and use algorithms from parallel computing to split the work and merge the results. That's how Google can create search results from billions of web pages in a few milliseconds.
See here for an introduction.
This is a very generic question and does not take the database backend into account. Firing with 40 or 1000 machines on a database backend that can not handle the load will give you nothing. Such a problem is truly to broad to answer it in a specific way..you should get in touch with people inside your organization with enough skills on the DB level first and then come back with a more specific question.
Assuming N computers, X files at about 50GB files each, and a goal of having 1 database containing everything at the end.
Question: It takes 15 hours now. Do you know which part of the process is taking the longest? (Reading data, cleansing data, saving read data in tables, indexing… you are inserting data into unindexed tables and indexing after, right?)
To split this job up amongst the N computers, I’d do something like (and this is a back-of-the-envelope design):
Have a “central” or master database. Use this to mangae the overall process, and to hold the final complete warehouse.
It contains lists of all X files and all N-1 (not counting itself) “worker” databases
Each worker database is somehow linked to the master database (just how depends on RDBMS, which you have not specified)
When up and running, a "ready" worker database polls the master database for a file to process. The master database dolls out files to worker systems, ensuring that no file gets processed by more than one at a time. (Have to track success/failure of loading a given file; watch for timeouts (worker failed), manage retries.)
Worker database has local instance of star schema. When assigned a file, it empties the schema and loads the data from that one file. (For scalability, might be worth loading a few files at a time?) “First stage” data cleansing is done here for the data contained within that file(s).
When loaded, master database is updated with a “ready flagy” for that worker, and it goes into waiting mode.
Master database has it’s own to-do list of worker databases that have finished loading data. It processes each waiting worker set in turn; when a worker set has been processed, the worker is set back to “check if there’s another file to process” mode.
At start of process, the star schema in the master database is cleared. The first set loaded can probably just be copied over verbatim.
For second set and up, have to read and “merge” data – toss out redundant entries, merge data via conformed dimensions, etc. Business rules that apply to all the data, not just one set at a time, must be done now as well. This would be “second stage” data cleansing.
Again, repeat the above step for each worker database, until all files have been uploaded.
Advantages:
Reading/converting data from files into databases and doing “first stage” cleansing gets scaled out across N computers.
Ideally, little work (“second stage”, merging datasets) is left for the master database
Limitations:
Lots of data is first read into worker database, and then read again (albeit in DBMS-native format) across the network
Master database is a possible chokepoint. Everything has to go through here.
Shortcuts:
It seems likely that when a workstation “checks in” for a new file, it can refresh a local store of data already loaded in the master and add data cleansing considerations based on this to its “first stage” work (i.e. it knows code 5484J has already been loaded, so it can filter it out and not pass it back to the master database).
SQL Server table partitioning or similar physical implementation tricks of other RDBMSs could probably be used to good effect.
Other shortcuts are likely, but it totally depends upon the business rules being implemented.
Unfortunately, without further information or understanding of the system and data involved, one can’t tell if this process would end up being faster or slower than the “do it all one one box” solution. At the end of the day it depends a lot on your data: does it submit to “divide and conquer” techniques, or must it all be run through a single processing instance?
The simplest thing is to make one computer responsible for handing out new dimension item id's. You can have one for each dimension. If the dimension handling computers are on the same network, you can have them broadcast the id's. That should be fast enough.
What database did you plan on using with a 23-dimensional starscheme? Importing might not be the only performance bottleneck. You might want to do this in a distributed main-memory system. That avoids a lot of the materalization issues.
You should investigate if there are highly correlating dimensions.
In general, with a 23 dimensional star scheme with large dimensions a standard relational database (SQL Server, PostgreSQL, MySQL) is going to perform extremely bad with datawarehouse questions. In order to avoid having to do a full table scan, relational databases use materialized views. With 23 dimensions you cannot afford enough of them. A distributed main-memory database might be able to do full table scans fast enough (in 2004 I did about 8 million rows/sec/thread on a Pentium 4 3 GHz in Delphi). Vertica might be an other option.
Another question: how large is the file when you zip it? That provides a good first order estimate of the amount of normalization you can do.
[edit] I've taken a look at your other questions. This does not look like a good match for PostgreSQL (or MySQL or SQL server). How long are you willing to wait for query results?
Rohita,
I'd suggest you eliminate a lot of the work from the load by sumarising the data FIRST, outside of the database. I work in a Solaris unix environment. I'd be leaning towards a korn-shell script, which cuts the file up into more managable chunks, then farms those chunks out equally to my two OTHER servers. I'd process the chunks using a nawk script (nawk has an efficient hashtable, which they call "associative arrays") to calculate the distinct values (the dimensions tables) and the Fact table. Just associate each new-name-seen with an incrementor-for-this-dimension, then write the Fact.
If you do this through named pipes you can push, process-remotely, and readback-back the data 'on the fly' while the "host" computer sits there loading it straight into tables.
Remember, No matter WHAT you do with 200,000,000 rows of data (How many Gig is it?), it's going to take some time. Sounds like you're in for some fun. It's interesting to read how other people propose to tackle this problem... The old adage "there's more than one way to do it!" has never been so true. Good luck!
Cheers. Keith.
On another note you could utilize Windows Hyper-V Cloud Computing addon for Windows Server:http://www.microsoft.com/virtualization/en/us/private-cloud.aspx
It seems that your implementation is very inefficient as it's loading at the speed of less than 1 MB/sec (50GB/15hrs).
Proper implementation on a modern single server (2x Xeon 5690 CPUs + RAM that's enough for ALL dimensions loaded in hash tables + 8GB ) should give you at least 10 times better speed i.e at least 10MB/sec.
Ok everyone, I have an excellent challenge for you. Here is the format of my data :
ID-1 COL-11 COL-12 ... COL-1P
...
ID-N COL-N1 COL-N2 ... COL-NP
ID is my primary key and index. I just use ID to query my database. The datamodel is very simple.
My problem is as follow:
I have 64Go+ of data as defined above and in a real-time application, I need to query my database and retrieve the data instantly. I was thinking about 2 solutions but impossible to set up.
First use sqlite or mysql. One table is needed with one index on ID column. The problem is that the database will be too large to have good performance, especially for sqlite.
Second is to store everything in memory into a huge hashtable. RAM is the limit.
Do you have another suggestion? How about to serialize everything on the filesystem and then, at each query, store queried data into a cache system?
When I say real-time, I mean about 100-200 query/second.
A thorough answer would take into account data access patterns. Since we don't have these, we just have to assume equal probably distribution that a row will be accessed next.
I would first try using a real RDBMS, either embedded or local server, and measure the performance. If this this gives 100-200 queries/sec then you're done.
Otherwise, if the format is simple, then you could create a memory mapped file and handle the reading yourself using a binary search on the sorted ID column. The OS will manage pulling pages from disk into memory, and so you get free use of caching for frequently accessed pages.
Cache use can be optimized more by creating a separate index, and grouping the rows by access pattern, such that rows that are often read are grouped together (e.g. placed first), and rows that are often read in succession are placed close to each other (e.g. in succession.) This will ensure that you get the most back for a cache miss.
Given the way the data is used, you should do the following:
Create a record structure (fixed size) that is large enough to contain one full row of data
Export the original data to a flat file that follows the format defined in step 1, ordering the data by ID (incremental)
Do a direct access on the file and leave caching to the OS. To get record number N (0-based), you multiply N by the size of a record (in byte) and read the record directly from that offset in the file.
Since you're in read-only mode and assuming you're storing your file in a random access media, this scales very well and it doesn't dependent on the size of the data: each fetch is a single read in the file. You could try some fancy caching system but I doubt this would gain you much in terms of performance unless you have a lot of requests for the same data row (and the OS you're using is doing poor caching). make sure you open the file in read-only mode, though, as this should help the OS figure out the optimal caching mechanism.
I have a system which is receiving log files from different places through http (>10k producers, 10 logs per day, ~100 lines of text each).
I would like to store them to be able to compute misc. statistics over them nightly , export them (ordered by date of arrival or first line content) ...
My question is : what's the best way to store them ?
Flat text files (with proper locking), one file per uploaded file, one directory per day/producer
Flat text files, one (big) file per day for all producers (problem here will be indexing and locking)
Database Table with text (MySQL is preferred for internal reasons) (pb with DB purge as delete can be very long !)
Database Table with one record per line of text
Database with sharding (one table per day), allowing simple data purge. (this is partitioning. However the version of mysql I have access to (ie supported internally) does not support it)
Document based DB à la couchdb or mongodb (problem could be with indexing / maturity / speed of ingestion)
Any advice ?
(Disclaimer: I work on MongoDB.)
I think MongoDB is the best solution for logging. It is blazingly fast, as in, it can probably insert data faster than you can send it. You can do interesting queries on the data (e.g., ranges of dates or log levels) and index and field or combination of fields. It's also nice because you can randomly add more fields to logs ("oops, we want a stack trace field for some of these") and it won't cause problems (as it would with flat text files).
As far as stability goes, a lot of people are already using MongoDB in production (see http://www.mongodb.org/display/DOCS/Production+Deployments). We just have a few more features we want to add before we go to 1.0.
I'd pick the very first solution.
I don't see why would you need DB at all. Seems like all you need is to scan through the data. Keep the logs in the most "raw" state, then process it and then create a tarball for each day.
The only reason to aggregate would be to reduce the number of files. On some file systems, if you put more than N files in a directory, the performance decreases rapidly. Check your filesystem and if it's the case, organize a simple 2-level hierarchy, say, using the first 2 digits of producer ID as the first level directory name.
I would write one file per upload, and one directory/day as you first suggested. At the end of the day, run your processing over the files, and then tar.bz2 the directory.
The tarball will still be searchable, and will likely be quite small as logs can usually compress quite well.
For total data, you are talking about 1GB [corrected 10MB] a day uncompressed. This will likely compress to 100MB or less. I've seen 200x compression on my log files with bzip2. You could easily store the compressed data on a file system for years without any worries. For additional processing you can write scripts which can search the compressed tarball and generate more stats.
Since you would like to store them to be able to compute misc. statistics over them nightly , export them (ordered by date of arrival or first line content) ... You're expecting 100,000 files a day, at a total of 10,000,000 lines:
I'd suggest:
Store all the files as regular textfiles using the following format : yyyymmdd/producerid/fileno.
At the end of the day, clear the database, and load all the textfiles for the day.
After loading the files, it would be easy to get the stats from the database, and post them in any format needed. (maybe even another "stats" database). You could also generate graphs.
To save space ,you could compress the daily folder. Since they're textfiles, they would compress well.
So you would only be using the database to be able to easily aggregate the data. You could also reproduce the reports for an older day if the process didn't work, by going through the same steps.
To my experience, single large table performs much faster then several linked tables if we talk about database solution. Particularly on write and delete operations. For example, splitting one table into three linked tables decreases performance 3-5 times. This is very rough, of course it depends on details, but generally this is the risk. It gets worse when data volumes get very large. Best way, IMO, to store log data is not in a flat text, but rather in a structured form, so that you can do efficient queries and formatting later. Managing log files could be pain, especially when there are lots of them and coming from many sources and locations. Check out our solution, IMO it can save you lots of development time.
I can not think of any reasons why we need to have multiple files inside a file group. The reason why I think of this way is we can control from T-SQL (end user) level about file group, but can not control from T-SQL (end user) level about individual files of a file group. Any comments or ideas why files are still needed?
thanks in advance,
George
Having multiple files per file group is only useful for the following reasons:
Distributing disk I/O load over multiple disks for performance reasons. i.e. in cases where re-configuring the RAID configuration with additional disks is not possible, or there is no RAID.
In cases where you have a VLDB and do not wish to deal with very large single files for logistical reasons.
There is 'urban legend' that SQL Server uses only 1 thread per file, so that the number of files should match the number of CPU's. This is however false, as discussed by Microsoft here.
Historically, there is another reason. Believe it or not in the days of SQL Server 4.2 through 7 sql server was sometimes installed on FAT32 file systems which had a 4 gig file limit. The ability to chain files together (in what we now call file groups) was a way to work around file system limitations and allow DBs larger than 4gigs on FAT based installs.
old thread, i know, but here is what makes sense to me: back in the day max file size in windows filesystem FAT32 was 2GB. If your database-file got bigger you were screwed (happened to me with a MS Access-Database once). Hence they allowed to define a max filesize (like: 2GB) and You could add more files. If your database grew and the max size got exceeded the next file got filled until that was full and so on. All those files can be addressed as one filegroup. You can define a tables data-location by choosing a filegroup, but you don't see in which file within that filegroup the tabledata will end up. All You know is that your tables data can end up in any of the files within the filegroup.
By this "splitting" Your filesystem never sees a file larger than the max filesize (here: 2GB) although tables in Your Database can be many times larger.
Today setting up multiple files can be useful to have large datafiles "chopped" into smaller pieces for filebased backup (ask your network admins what they want, because during backup writing a large (like 1TB) file into a partition takes long, even in fast RAID. All other writing operations would need to wait a long time. Shorter waiting intervals let high prioritized operations come to execution quicker).
If You care for parallel access of the same table consider horizontal partitioning as in http://msdn.microsoft.com/en-us/library/ms188730%28v=sql.105%29.aspx. this allows to spread the data of a table over different harddisks, like "all sales of January on disk R:", "all sales of February on disk S:", without creating separate tables. During the procedure of partitioning of a table You can define which part shall go to what filegroup.
I could provide a long explanation but MSDN does a good job of it here. It may be that you specifically don't need to have more than one file in a file group, but that is not true of everybody.