Category Archives: MySQL

Hinweise zu MySQL

MySQL: LOAD DATA INFILE, multiple csv-files and index creation for big tables

Posted on by

In some of the last articles in this blog I have discussed the following aspects of a data import from csv-files into MySQL ISAM tables:

  • The use of the SQL command “LOAD DATA INFILE” may accelerate the import by a factor of at least 18 in comparison to reading the data line by line with fgetcsv() and inserting single records into the database table.
  • The creation of a (small) helper table may often be useful to improve the performance of repeated consistency checks based on the evaluation of “derived result sets” from COUNT(DISTINCT …) subselects or subqueries on big tables.

The first point implies a straightforward use of “LOAD DATA INFILE” in your PHP programs. The second point may be applicable if certain data structures are given and it requires a balanced use of indices.

See:
Importing large csv files with PHP into a MySQL MyISAM table
MySQL/PHP: LOAD DATA – import of large csv files – linearity with record number?
MySQL/Aggregation: Comparing COUNT(DISTINCT) values on big tables

In these articles I have more or less neglected two things:

  • A possible splitting of the import process into several sequential processes. Instead of loading one big file we may rather distribute the data records over several files. Each individual process loads data from just one file; so the data are loaded file after file into the table.
  • The impact of index creation on the data import time.

Reasons for a sequential data loading from multiple csv-files are: Better control of the loading process due to shorter times for well defined data packages and also an adaption to memory limits. However, without tests it is not clear whether such an approach may have an impact on the overall loading performance.

The second point raises the question whether and how index creation influences the efficiency of “LOAD DATA INFILE”. Note that the MySQL documentation explicitly recommends to separate index creation from the loading process. See:
http://dev.mysql.com/doc/refman/5.1/de/load-data.html

This article provides some numbers from test runs for both measures. We shall see how the combination of both points influences the overall performance of the data import. Furthermore, an optimized loading process may influence your strategy regarding consistency checks during a sequential loading of several files.

Loading data from a sequence of multiple files instead of loading from one big file

I briefly have a look at some of the aspects of loading data from a sequence of files. In the article PHP/MySQL/Linux: File upload, database import and access right problems I have already discussed that one may split the data to be imported into bunches of records and distribute them over several files. I have pointed out that the data splitting shall not be done by fields. Instead records should be distributed over multiple files; but each record should contain all of the associated (!) fields if you want to profit from the performance of “LOAD DATA INFILE”.

A further aspect you have to take care of are logical relations between the data records. Such relations may become relevant in case you already want to work with the data in between 2 file loads:

Lets say you have
data with the structure

nr,    m,n,    i    Q1, Q2, Q3

m,n,i shall represent keys which together form an unique key of each record. An [m,n]-combination may e.g. define an object called “node” and [i] may represent time periods. Let us further assume that after having loaded one file you for some reason want to check the complete SUM (Q2) over all (!) i-values for each “node” before you (or a PHP program) import the data of the next file. Then you must organize your data such that all records related to one and the same node are placed inside exactly one file – otherwise you would get a moving target for your SUM when loading your files. In general, the distribution of the records over several files must comply with logical conditions if you want to work with the data already during the loading process.

Another aspect of distributing records over several files may be memory considerations: In one of the articles named above we saw that the loading and index generation consume more and more memory for buffering as the absolute numbers of records grow. Therefore, if you want to avoid conflicts with limited RAM, it may be reasonable to load bunches of records (in form of several csv-files) instead of processing on one gigantic file with “LOAD DATA INFILE” .

In most cases the central objective of using data packages is a better control of the file transfer and loading processes: With multiple files you can build up a “file pipeline” on the server. The files in this “pipeline” are handled by a repeatedly running import job. The required time interval to transfer each file to the server and load its data into the base can be limited to a reasonable value by choosing an appropriate number of records per file. After each file is processed on the server you may analyze the new table status, the pipeline status and send success or failure messages to the client (e.g. a browser). With a web client the communication exchange can be managed by Ajax.

A sequential loading of files also provides you with the possibility to fill the file pipeline on the server in parallel to loading data of already completely received files into the database. Despite a more complicated communication structure between a browser client and the server you can still keep control over both the transfers from the client to the server as well as over a sequence of server processes loading loading data into the base – file by file.

I have realized the sequential handling of several csv-files on a web-server with Ajax and PHP in the following way:
A web client starts an Ajax controlled PHP job that analyzes the number of files in a pipeline (a special directory) on the server and chooses the next file to import by some given rules. After having loaded the data the job returns control to the client and sends information about the processed file, the loaded number of records and the new status of the database table. Depending on parameter settings there and of course depending on the quality of the status information from the server a new server import job for the next file may automatically be started from the client with the help of Ajax from the client. This continues until the pipeline is empty. The last job may create required indices on the filled database table – depending on user settings.

Is the total import time independent of a record distribution over multiple files?

In the article MySQL/PHP: LOAD DATA – import of large csv files – linearity with record number? I discussed the seemingly linear dependency of “LOAD DATA INFILE” on the total record number of ONE file. Now, we should in addition investigate possible performance variations for import processes

  • that load a given number of records sequentially loaded from
    several csv-files;
  • with or without a parallel building of (unique) indices on defined columns.
  • that perform the creation of required (unique) indices only AFTER all records have been loaded into the database table.

Importing 5 million records from one or two files

Let us again take the data structure

nr,    m,n,    i    Q1, Q2, Q3

All Q-values are floating point numbers with double accuracy (MySQL type DOUBLE) in our scenario. We assume that at the beginning of our import runs we have already defined some indices on our database table:

  • a unique index (with auto-incrementation) on the column “nr”
  • a unique index defined for the columns “[n,m,i]”

In the course of our tests we shall study the effect of

  • dropping these indices,
  • creating them whilst filling the table with data,
  • creating them after having filled the table with data.

For each test case with 5 million records we look at two different situations :

  • Importing one csv-file with all 5 million records.
  • Sequentially importing 2 csv-files – one with 3 million records and one with 2 million records.

Our test server is a relatively small one in form of a KVM virtualized LAMP server with 2 GByte assigned RAM, CPU INTEL Q9550, Raid 10 disk array. For the tests I worked with MySQL MyISAM tables. In my concrete test table I chose:

distinct m values : 1 (as an extreme situation)
distinct n values : 13580
distinct i values : 386

I got the following numbers:

import by 1 file import by 2 files with unique ai-index on nr with non-unique index on nr without any index on nr with unique index on [n,m,i] with non-unique index on [n,m,i] without any index on [n,m,i] response time
yes no yes no no yes no no 30 secs
no yes yes no no yes no no 30 secs
yes no no yes no yes no no 30 secs
no yes no yes no yes no no 30 secs
yes no no yes no no yes no 21,3 secs
no yes no yes no no yes no 24,8 secs
yes no no no yes yes no no 19.2 secs
no yes no no yes yes no no 19.4 secs
yes no no no yes no yes no 9.8 secs
no yes no no yes no yes no 13.3 secs
yes no no no yes no no yes 8.7 secs
no yes no no yes no no yes 8.8 secs

The accuracy of the response times is in the range of a second – depending on the general situation on the virtualization host and its disk system at the measurements. The response times do not include any file transfer times between the client and the server in our LAN. However, the response time for the situations with a sequential load of several files includes some common Ajax overhead in all cases plus the time to load the PHP program files into our Apache server again. But the system buffers and caches sufficiently. So, the interaction between the client and the server should only have a marginal impact.

What do we learn from the results ? I dare to derive 4 rules:

  • Rule 1: Any unique index built during “LOAD DATA INFILE” reduces performance (due to checks which have to be performed). So, one should carefully check whether a unique index really is required later on when you work with the data.
  • Rule 2: The total import time for several or one big file are comparable if a unique index is created whilst or after data loading.
  • Rule 3: Any building of a non-unique index during the import increases the total import time for a sequential load of several csv-files in comparison to a loading process for just one big csv-file.
  • Rule 4: Without any index defined the total loading time for several files is comparable to the loading file for one big csv-file.

The most surprising effect for me was that described in rule 3. However, regarding the accuracy in the range of a second we may have some doubts whether we see a real effect. So, we should have a look at a situation with more data records and more files to confirm this rule.

Now, it will of course be interesting to see a comparison between different loading processes with a creation of the [n,m,i]-index AFTER the processing of all files with “LOAD DATA INFILE”. For such cases technically we drop any index before we load any file data and create the index only after the data of all files have been loaded into the base. Note, that index dropping may take some time, too.

Table: Comparison of server response times for index creation AFTER and WHILST data loading 5 million records with “LOAD DATA INFILE”

n

import by 1 files import by 2 files without any index on nr with unique index building on [n,m,i] whilst loading with non-unique index building on [n,m,i] whilst loading with unique index creation on [n,m,i] after loading with non-unique index creation on [n,m,i] after loading response time
yes no yes yes no no no 19.2 secs
no yes yes yes no no no 19.4 secs
yes no yes no yes no no 9.3 secs
no yes yes no yes no no 13.2 secs
yes no yes no no yes no 19.5 / 18.5 secs
yes no yes no no yes no 20.7 / 19.1 secs
yes no yes no no no yes 11.9 / 10.8 secs
no yes yes no no no yes 11.4 / 11.1 secs

The first time value given in the last four lines includes an index drop time which varies a bit according to precise server conditions at the time of measurement.

The most interesting result is that the differences measured between a sequential load of several files and a situation with just one big file almost to disappear when we create the index after the pure data loading.

Importing 10 million records from one or five files

Now, let us turn to 10 million records and an equal distribution of records over 5 files (with 2 million records each) and see whether we confirm our results. For the test we have just doubled the number of nodes in comparison to the 5 million case.

Table: Server response times for loading 10 million records with “LOAD DATA INFILE”

import by 1 files import by 5 files without any index on nr with parallel unique index building on [n,m,i] with parallel non-unique index building on [n,m,i] with unique index creation on [n,m,i] after loading with non-unique index creation on [n,m,i] after loading response time
yes no yes yes no no no 42 secs
no yes yes yes no no no 42,4 secs
yes no yes no yes no no 20 secs
no yes yes no yes no no 37 secs
yes no yes no no yes no 43.3 / 42.0 secs
no yes yes no no yes no 41.3 / 40.0 secs
yes no yes no no no yes 22.9 / 21.8 secs
no yes yes no no no yes 22.5 / 21.4 secs

Again, we see the huge impact of the creation of an unique index – but the total
loading time is almost independent of whether you create the index whilst or after the data loading. This is understandable as the required checks for a uniqueness must be done in both situations.

However, at least for me, it was almost shocking to see the difference between lines 3 and 4:

There is a big difference between a sequential loading process for several files and a loading process for just one file, when a non-unique index was created whilst loading with “LOAD DATA INFILE”.

Note in contrast the considerable reduction of the required time if you create the non-unique index after all data have been loaded into the database. The measure discrepancy in total loading time is really remarkable – and actually I have no convincing explanation for these findings. The tree structure of the index is quite simple; I do not really see what costs the overhead. And I have tested the effect several times to exclude any extraordinary conditions on the server. I have also validated parameter settings and the steps done during the sequence of loading steps to exclude any unplanned work or actions on the server.

The [n,m,i] index in my case has a size of 215 MByte; the data themselves take another 400 MByte. All in all we speak about 600 MByte. So, I suspect some reading and writing from/to the disk at the end/beginning of the individual 5 loader jobs and some inefficient memory management. I have not yet investigated this in detail. Write me a mail if you know the reason.

Independent of the reasons: For me, it was really important to see this difference because I do not need a unique index and I want to work with many more files to cover much larger numbers of records.

So, our result is:

You should avoid the creation of a non-unique index whilst using “LOAD DATA INFILE” on multiple files by sequentially started (PHP) jobs on the server instead of loading just once from one file. Whenever you load your data from several files create the index after all data from all files have been imported into the database table.

Impact on consistency checks during a sequential load of several files

In the article MySQL/Aggregation: Comparing COUNT(DISTINCT) values on big tables I discussed the creation of a helper table and respective indices to get a good performance of consistency checks. Most of these consistency checks were based on an analysis of derived result sets for “SELECT COUNT(DISTINCT … ).. ” statements. My basic idea was to perform such checks after each import process when dealing with a sequence of files.

However, in the named article I also showed that a fast creation of a helper table required already some existing index on the big data table. But our present results favor a solution where an index is created only after all data of all files have been loaded. So, no intermediate consistency checks in between the loading processes for two csv-files !

After some thinking I came to the conclusion that this is no big disadvantage. Even if you consistency checks only after all data have been loaded, you will still be able to identify the records which are problematic. And with some effort, data and file analysis – which will cost some extra time, but maybe worth it – you even may find the file from which the record was loaded. So, you can give the user who tried to load the files a full report on the suspicious records and that caused inconsistencies and the files that contained them.

Conclusion

We have tested different combinations for loading data with one or multiple files and with or without creating indices during/after the data loading. Our tests have shown that if you import data sequentially from several
files and you want to create a non-unique index over some columns, it makes a huge difference whether you create this non-unique index during or after the sequential “LOAD DATA INFILE” processes. One should avoid creating such indices whilst loading the data with “LOAD DATA INFILE”. It should be created after all data from all files have been imported into the database table.

In contrast such an order of steps does not seem to be required when creating unique indices. However, a unique index costs in general significantly more time to be created than a non-unique one. You, therefore, should evaluate the necessity of unique indices.

MySQL/Aggregation: Comparing COUNT(DISTINCT) values on big tables

Posted on by

Sometimes one needs to work with rather big database tables and with COUNT(DISTINCT) statements. Recently, I had to analyze the result sets of COUNT(DISTINCT) statements for table consistency checks. These tasks required subselects or subqueries. The SQL statements were performed on a test table with more than 5 million records. The data records – together with data of another table – formed the data basis of several complicated computational runs. The eventual record number in our application will be much larger, but some optimization principles can be learned already from 5 million example.

Most of the computed COUNT(DISTINCT) values referred to some of the table’s key columns. Our data records have a combination of three different key fields, which together define a unique key. The combined unique key has a sub-structure corresponding to physical aspects of the real world data objects behind the records. This induces a natural grouping of records.

Such a data structure occurs very often in physical simulations: Think e.g. of a grid of meteorological observation stations that measure environment data at distinct points of a time line. Each station would then be characterized by 2 positional data. The measured quantity values would have a three fold unique key : two geometrical position data and a time value. Then the data for temperature, pressure over time in a database table could be aggregated and grouped according to geometrical positions, i.e. for the stations and their locations.

In my case COUNT(DISTINCT) values had to be computed over one key column and for groups defined by distinct combinations of other keys. The results should be compared with a reference value: the COUNT (DISTINCT) result for the whole table. I shall explain the detailed key structure and the comparison objectives below. Some of the counted distinct values also had to be compared with similar values of another, but much smaller table that contained only around 14000 records for a reduced set of the key columns. The comparisons were part of consistency checks concerning record groups inside the huge table and concerning the equality of distinct key values used in the huge table and the smaller table.

When working on these tasks I first tried to optimize the required sub-select statements for the COUNT(DISTINCT) evaluation on my key columns by defining and using proper indices. After some more analysis, I extended my optimization strategy to creating a helper table instead of working with intermediate but volatile “derived result sets” of my sub-selects. Read below, why …

My case is a little special, but consistency checks regarding the number of records in certain record groups which correspond to distinguished combinations of some keys occur in very many environments (physical simulation, statistics, neuronal networks, ..). Therefore, I hope that the insights I got from my simple scenario may be helpful for other people who have to deal with COUNT(DISTINCT) statements on big tables, too.

The playground, data key structure and some numbers

In our project we load the data of a sequence of csv-files (each with millions of records) into 2 tables of a database. After each import process we check some consistency conditions the records of two tables must fulfill. The reason is simply that we want to avoid interrupted or crashing computation runs due to a lack of data or inconsistent data provided by our tables.

Each record in our big table contains data values that depend on a 3 fold key-structure:

Two keys [m,n] describe certain objects by 2 dimensions in an abstract space. Let us call such an object a “node”. For a “node” some hundred values of several quantities are defined with respect to an additional integer key “[i]” of a further dimension.

The table structure
of our first table “TA” is flat – something like

nr,    m, n, i    Q1, Q2, Q3, ….

with the Qs representing some quantities and the first column being a auto-indexed one. The number distribution of the key values in our case is as follows:

m has only some few distinct values (2 to 10), whereas n may have distinct values in a range between 10000 up to 100000. Distinct i values are in the range between 200 and 1000. In my concrete test table I chose:

  • distinct m values : 1 (as an extreme situation)
  • distinct n values : 13580
  • distinct i values : 386

There is another table “TB” with keys [m,n] and associated quantity values per object. The data of both tables are later on used to perform several calculations. For a sufficient performance of the computational runs we need at least a combined index over the tupel [m,n] – which should be created already during the data import phase. The funny thing in our scenario is that the time required for the data load and import phase is dominant in comparison with all of our computation runs – right now by a factor of 1,5 to 3. So, we really have no intention of making the import time interval any bigger without very good reasons.

Objectives of COUNT and COUNT (DISTINCT ) statements : consistency checks

The consistency of the imported data in the two tables TA and TB must be guaranteed. Some of the consistency conditions that must be fulfilled are:

  1. The number of distinct values for both [m], [n] and [m,n] must be the same in the tables TA and TB
  2. The number of unique [m,n,i] values should be equal to the total number of entries in table TA.
  3. The number of distinct [i] values (i.e. number of distinct records) for any given [m,n]-key pair (i.e. node) should be equal to the total number of records for that [m,n]-pair.
  4. The number of distinct i values (i.e. number of records) of a given [m,n]-key pair in table TA should have an identical [m,n]-independent value. (For all nodes there must be an identical number of i-dependent records).
  5. The number of distinct i-key values of any [m,n] pair should be identical to the number of distinct i-values given for the full table

The last 3 conditions together (!) guarantee that for each [m,n] combination (=node) the different i values of the associated records

  • are all distinct – with the same sequence of values independent of [m,n];
  • lead to the same number of records for every [m,n]-combination.

The following aspects should be considered:

  • The checks are to be performed several times as the big table would be generated stepwise by a sequence of “LOAD DATA INFILE” processes. E.g. 4 times a 5 million record file. A consistency check should in addition always be performed before a computation run is started.
  • The time required for consistency checks add up to the total data import and preparation time for the tables. The effect should be limited to a tolerable factor below 1.3.
  • We expect indices over the key columns to play an important role for the performance of SELECTS for aggregated distinct key values. Building indices, however, may cost substantial additional time during the data import and preparation phase.
  • The performance of certain SELECT statements may depend on the order of columns used in the index definition.

As the data import and preparation time was already dominant we were eager to avoid any intolerable prolongation of this time period.

The impact
of a unique or non unique index over all key columns on the data import and preparation time

In our case we would create a [m,n] or [n,m] index. Such an index was unavoidable due to performance requirements for the computation runs. An [n,m]-index would give us a slight performance advantage during our calculations compared to an [m,n] index. But this difference is marginal. In the data loading phase we import our data into the MySQL base with “LOAD DATA INFILE” statement issued by a PHP program (see ). The index is filled in parallel. We were interested in whether we could afford a full unique index over all 3 key tables.

Below we give some numbers for the required time intervals of our data imports. These numbers always include a common overhead part. This overhead time is due to an Ajax exchange between browser and server, zip file transfer time across our network, PHP file loading times, zip expansion time on the server, movement of files … So the numbers given below do not reflect the pure data loading time into the base on the server. By estimation this time is at least by 4,5 seconds smaller. The test server itself is a relatively small one in form of a KVM virtualized LAMP server with 2 GByte assigned RAM, CPU INTEL Q9550, Raid 10 disk array. For the following tests I worked with MySQL MyISAM tables – however, the general picture should hold for InnoDB tables, too.

Addendum, 21.09.2014:
Please note in addition that our big table has a first column which is auto-incremented and is used as a primary index. This primary index was always created. It explains why the absolute numbers of data import times given below are relatively big. See a forthcoming article
MySQL: LOAD DATA INFILE, csv-files and index creation for big data tables
for a more complete discussion of the impact of (unique and non-unique) indices on data imports from csv files.

We got the following loading times required to fill table TA with data from a csv-file with m=1, n=13580, i=386 (all distinct) by using “LOAD DATA INFILE”:

  • without [n,m,i]-index creation : 23.5 sec.
  • with [n,m]-index creation (non unique) : 25.3 sec
  • with [n,m,i]-index creation (non unique) : 26.3 sec
  • with [n,m,i]-index creation (unique) : 36.2 sec

(compare with the following blog articles
MySQL/PHP: LOAD DATA – import of large csv files – linearity with record number and
Importing large csv files with PHP into a MySQL MyISAM table )

Note that there is no big difference between creating a full [n,m,i]-index over all key columns and creating an index only for the first two columns of our test table. So a full [n,m,i]- index creation seemed to be affordable. It would not hamper the computation runs, but it helps a little with our consistency checks.

However, there is a substantial difference of loading times for a unique and a non unique index. This difference stems of course from related checks which have to be performed during the implicit INSERTs of the data into the table. As we do not really need a unique index for the computation runs the question arises whether we can just use a plain index and still can verify check condition 2 with a better performance than using 10 extra seconds. The answer is – yes we can do that. See below.

Another remark for reasons of completeness:

An [i,n,m]-index instead of a [n,m,i]-index would double the time required for our computation runs. The branching structure of an index tree is performance relevant! in our scenario this is due to the fact that aggregations (sums, statistical information, … ) over the i-dependent quantities for a node play an important role in the calculation steps. Selects or aggregation for distinct [n,m]-nodes and aggregation processes over [n,m]-related record groups require a [n,m,..] index. In case of doubt about the n,mm-order I would say it is better to start with the more numerous branching. Any initial branch chosen then leads to a smaller amount of remaining records for further analysis. Almost all statements discussed below profit from the chosen column and branching order of the index.

What information is really required?

Looking at condition 2 we would say: Let us get the number of all distinct [n,m,i] triples in the table and compare that with the COUNT(*) value for the table. With our given [n,m,i]-index, we could use the following SQL statement:

“SELECT COUNT(DISTINCT n,m,i) FROM TA” :: 1.9 to 2.0 secs .

If we had a unique index the answer would come much faster. However, with our plain [n,m,i]-index such a statement takes 2 secs – which is still considerably less than 10 secs (< 10 secs!). Our [n,m,i]-index is used already for grouping and sorting as EXPLAIN will tell you. So, most of the time is spend for comparing i values of the sorted groups. If there were no other conditions we would have to use this statement. However, conditions 3 to 5 are very strict. Actually, some thinking shows that if we guarantee conditions 3 to 5 we also have guaranteed that the total number of records is identical to the number of distinct records. If for some given [m,n] combination we have y distinct records than we have y different i-values. The rest follows ... Therefore, we should instead concentrate on getting information about the following numbers:

  1. The total number of distinct i values in the table. This would be used as a reference value “$ref_dist_i”.
  2. The total number of i-values ( = number of records ) “num_i” per [m,n]-node (for all nodes).
  3. The total number of distinct i-values “num_dist_i” per [m,n]-node (for all nodes).
  4. The number of records, for which num_dist_i or num_i deviate from $ref_dist_i.

If we find no deviation than we have also confirmed condition 2.

“Alternative”
There is another way to prove condition 2 if there is an additional precondition:

The sequence of i-values given for each node shall be the same for all nodes.

This condition is given in very many circumstances (e.g. in physics simulations) – also in our case. Then we could look at the difference of

Max(Distinct i) – Min (DISTINCT i) for each node

and demand

  • [Max(Distinct i) – Min (DISTINCT i) + 1] == [COUNT (DISTINCT i)] == [COUNT i] per node
  • plus that [COUNT(i)] has the same value for all nodes.

Please note that we would not need to determine the number of distinct i values in our big table at all for this variant of our consistency check.

We shall see below why this can give us a substantial advantage in terms of SELECT times.

To investigate condition 1 we need additionally:

  • The total number of distinct [n,m]-values, distinct m-values and distinct n-values for the tables TA and TB.

Aggregated distinct key values for groups corresponding to leading branches of an index

Let us now consider
SQL statements that determine distinct [n,m]-values, distinct m-values and distinct n-values of table TA. We make the following trivial assuption: COUNT (DISTINCT ) aggregations over one or several columns are much faster if you can use an index defined for these columns. Why ? Because an index has a tree structure and “knows” about the number of its branches which are determined by unique values! Furthermore, also on subsequent branching levels it can be used for grouping and sorting of records ahead of internal value comparisons.

If this is true we would further assume that getting the number of distinct [n,m] values should be pretty fast, because our [n,m,i]- index can be used. And really :

“SELECT COUNT(DISTINCT n,m) FROM TA” :: 0.051 to 0.067 secs

The same holds for “n” alone:

“SELECT COUNT(DISTINCT n) FROM TA” :: 0.049 to 0.067 secs

The marginal difference is not surprising as there is only one [m]-value. EXPLAIN shows that in both statements above the index is used for internal grouping and sorting.

However :

“SELECT COUNT(DISTINCT m) FROM TA” :: 1.4 to 1.6 secs

What we see here is that the index may still be used for grouping – but for each “n”-branch the “m”-index values still have to be compared. Can we make this evaluation a bit faster? What about a sub-select that creates a [n,m]-basis for further analysis ? This could indeed lead to a better performance as ordered [n,m]-pairs correspond directly to leading branches of our index and because the result set of the subquery will be significantly smaller than the original table:

“SELECT COUNT(DISTINCT a.ma) FROM (SELECT DISTINCT n, m AS ma FROM TA ORDER BY n) as a” :: 0,056 to 0,078 secs

Compared to a more realistic (n,m)-value distribution this result is a bit too positive as we only have one distinct m-value. Nevertheless, we see that we can use our already created [(n,m),i]-index with some considerable advantage to work on parts of condition 1 in the list above. Note, that a temporary table for the derived result set of the sub-select is created; EXPLAIN and time measurements of the MySQL profiler will show that. This intermediate table is substantially smaller than the original table and also the number of distinct [m]-values is small (here 1).

What about table TB and these numbers ? Well, TB is just as small as our intermediate table of the last statement! And because it is (relatively) small, we can afford to create whatever index required there – one for [n], one for [m], one for [n,m]. So, we can get all required node information there with supreme speed.

Summary: We can perform consistency checks for condition 1 without any performance problems. Our [n,m]-index is suitable and sufficient for it.

Two additional lessons were learned:
1) Aggregation for key groups and leading index branches over corresponding key columns should fit to each other.
2) Intermediate temporary tables may help for a required aggregation – if they are much smaller than the original table. This leads to SQL statements with subselects/subqueries of the form

SELECT COUNT(DISTINCT a.x) FROM ( SELECT DISTINCT col1 AS x, col2, .. as FROM ….ORDER BY .. ) as a

Aggregating distinct values of a column which is the last in the column definition order of a combined index

Now, let us turn to the determination of the total number of distinct i values. We need this value as a reference value ($ref_dist_i) if we want to verify conditions 3 to 5 ! The fastest way – if we only have our plain, non-unique [n,m,i]-index to get this number – is a simple

SELECT COUNT(DISTINCT i) FROM table TA : 1.85 – 1,95 secs

nAn EXPLAIN shows that this statement already uses the [n,m,i]-index. I did not find any way to make the process faster with just our plain (non unique) [n,m,i]-index. And note: No intermediate table would help us here – and intermediate table would only be a reordered copy of our original table. Therefore:

“SELECT COUNT(DISTINCT a.ia) FROM (SELECT DISTINCT i AS ia, n, m FROM TA ORDER BY n, m ) as a” :: 11.9 secs

To try to make the determination of COUNT(DISTINCT i) faster we could be tempted to create another index which starts it’s tree branching with the i-column or just a plain index for the i-column. We would build such an index during the data load process. And then we would find in our scenario that the extra time to create such an index would be ca. 2 sec. We would first have to invest time in a proper data analysis – even if a subsequent SELECT COUNT(DISTINCT i) FROM table TA would be pretty fast.

So, if we do not get the number of distinct i-values from elsewhere (e.g. from the provider of our csv-files) – we are stuck to something like an additional overhead of 2 secs – which gives us a choice: Create an additional index OR use the simple SQL statement ?

Note that even the creation of a pure i-index would consume considerable memory space on the hard disk (in my concrete example the required index space grew from 138 MB to 186 MB – this total value being not far from the total amount of data in table TA.) Because, up to now I have not found another usage for an i-index I refrain from creating it. I better save the determined number of distinct i values in a tiny additional parameter table to make it accessible for different PHP programs afterwards.

Comparing aggregated COUNT(DISTINCT) values for record groups with a given reference value

Conditions 3 to 5 of our task list require that we compare the reference value [$ref_val_i] for distinct i values of table TA with both the distinct i values and the sum of all i values for each of all the [m,n]-nodes. This leads us to something like the following SQL statement with a subselect/subquery:

Comparison Statement:

SELECT count(*) FROM ( SELECT COUNT(distinct b.i) AS sum_distinct_i, COUNT(b.i) AS sum_i FROM TA b GROUP BY b.n, b.m HAVING sum_distinct_i != $ref_dist_i OR sum_i != $ref_dist_i ) AS a

It gives us the number of nodes with deviations either of the number of associated distinct i-values or of the number of associated i-values from the reference value (here $ref_dist_i). The complicated statement requires some time.

Comparison statement :: < 2.2 secs

Despite the fact that our [n,m,i]-index is used as an EXPLAIN will show. You won’t get that much faster. The alternative

SELECT count(*) FROM ( SELECT COUNT(distinct b.i) AS sum_distinct_i, COUNT(b.i) AS sum_i FROM TA b GROUP BY b.n, b.m ) AS a WHERE a.sum_distinct_i != $ref_dist_i OR a.sum_i != $ref_dist_i

has the same performance. The slow part is the WHERE or HAVING analysis on the derived temporary result set.

By the way:
Working with an [n,m]-index and a separate [i]-index would slow down the comparison statement to > 2.6 secs. So, here we eventually find a slight advantage of our combined index over three columns (also the computation runs get a bit faster). In addition the combined index takes less disk space than several individual indices.

We now should take into account the following aspect of our scenario: We may call such a comparison statement several times if we load a sequence of data files – each with 5 million data records. Then our “comparison statement” will get slower and slower the more records the table comprises. In addition we may also start every computational run with a consistency check of the tables
and show the results in a browser! Then we need to optimize the response time for the web page. Even 2.2 secs may then be too much. How can we meet this challenge ?

Helper table instead of derived result sets from subselects/subqueries

My answer was to use a helper table “TC” – which receives the data of the derived result set from the sub-select of our comparison statement. In table TC we would store something like

m,    n,    sum_distinct_i,    sum_i

and maybe other node dependent values. This has also the advantage to become flexible in case that some day the distinct i values get [n,m]-dependent and have to be compared to some other values.

Note that the number of values in such a table is considerably smaller than in the original table. So, speaking in relative numbers we could generate some indices for [m,n], m, n, sum_dist_i, sum_i without much investment into cpu time and memory! This would also relieve us from complicated statements to determine the number of distinct m values as discussed above. And statements as

SELECT COUNT(*) FROM TC WHERE sum_distinct != $ref_dist_i OR sum_i != $ref_dest_i

would perform very well. (Under our present conditions this should give a plain zero if consistency is given).

Therefore, the only question remains: What would such a helper table cost regarding time ? The proper statement to fill a prepared table TC with indices is

INSERT INTO TC (m, n, sum_distinct_i, sum_i) SELECT a.m, a.n, COUNT(DISTINCT a.i), COUNT(a.i) FROM TA a GROUP BY a.n, a.m

This takes approximately 2.26 secs. However, an additional

SELECT COUNT(*) as sum_dist FROM TC WHERE sum_distinct_i != $ref_dist_i OR sum_i != $ref_dist_i

takes only 0,002 secs:

  • SELECT COUNT(DISTINCT m) FROM TC :: < 0.002 secs
  • SELECT COUNT(DISTINCT n) FROM TC :: 0.018 to 0.026 secs
  • SELECT COUNT(DISTINCT n,m) FROM TC :: 0.007 to 0.012 secs

This means that we do no longer have to be afraid of repeated consistency checks!

“Alternative”
If our conditions are such that we can follow the alternative discussed above the helper table is of even more help. The reason is that we can omit the determination of our reference value $ref_dist_i. Instead we fill the (modified) helper table with

INSERT INTO TC (m, n, sum_distinct_i, sum_i, min_i, max_i) SELECT a.m, a.n, COUNT(DISTINCT a.i), COUNT(a.i), MIN(DISTINCT i), MAX(DISTINCT i) FROM TA a GROUP BY a.n, a.m

This costs us only slightly more time than our original value. With a proper index generated over the columns sum_distinct_i, sum_i, min_i, max_i we the use

SELECT COUNT(DISTINCT sum_distinct_i, sum_i, min_i, max_i) FROM TC :: < 0.002 sec

In case that everything is OK this should give us a “1” in almost no time. Then additionally we fetch just one line

SELECT (*) FROM TC LIMIT 1

and check the conditions [Max(Distinct i) – Min (DISTINCT i) + 1] == [COUNT (DISTINCT i)] == [COUNT i] for this node, only.
The big advantage is that we win the time otherwise spent to determine the COUNT(DISTINCT i) value for our big table. This advantage gets bigger with a growing table.

Remark regarding indices: As you have realized we clutter the helper table with indices over various columns. Normally, I would characterize this as a bad idea. But in our case I do not care – compared to other things it is a small prize to pay for the relatively small space the numerous indices consume for our (relatively) small helper table.

The resulting sum of additional time investments for preparing the consistency checks

We
find for our 5 million record table that we need to invest some extra time of

  • around 2.2 secs for creating a helper table

in the best case. If we cannot use the “Alternative” we additionally need around 2 secs for getting the number of distinct i-values in our table.

So, this makes 1% in the best case up to 16 % of the original loading time (26.3 secs). I think, this is a good compromise as it gives us a repeatable consistency check for a given status of the tables.

Extrapolation to table enlargements resulting from loading a sequence of further data files

What happens if we deal with larger data amounts sequentially loaded into our big data table from several csv-files? We expect things to behave reasonably well:
A critical point is that the helper table TC should only get new records originating from the new file loaded. Already saved aggregated values should not be changed. This in turn requires that the data in the new files belong to nodes that are distinct from already loaded nodes. So, the data imported must be organized by nodes and systematically spread over the files to be loaded. The relevant data records for the new aggregations can easily be identified by an evaluation of an auto-index column in the big data table before and after the loading process. The aggregation results for the new nodes are than added to the helper table without any interference with the already aggregated values stored there.

Therefore, the time to generate additional entries in our helper table should remain a constant per csv-file to be imported!

However – if we need to do it – the determination of the COUNT(DISTINCT i) would require more and more time for our growing big table.

So, at this point we will really profit significantly from the discussed “Alternative”.

The time to perform our checks on the helper table will also grow – but in comparison to other times this remains a really small contribution.

Summary

In our scenario we had to find a compromise between index generation and a resulting prolongation of data loading and preparation time versus a faster consistency check (partially) based on aggregated COUNT(DISTINCT) values for (grouped) key columns. Such checks typically lead to sub-selects with derived result sets. Even if the temporary tables implicitly used by the RDBMS engine are relatively small compared to the original big tables any further aggregation or analysis on the derived data may cost considerable time. If such analysis is to be done more often than once the creation of a helper table with aggregated data may pay off very quickly.

In addition we have seen that one should carefully evaluate alternatives to verify consistency conditions and what information can and should be used in the verification process. Not all obvious ways are the best in terms of performance.

Addendum, 21.09.2014:
Is this the end of a reasonable line of thinking ? No, it is not, if you want to make your big data imports faster.
Creating a helper table is useful. But there are more points to take into account when trying to optimize:

The reader has probably noticed that the creation of a unique [n,m,i]-index enlarged the loading time drastically. This observation sets a huge question mark behind the fact that I had a primary index on the first auto-incremented “nr”-column. Furthermore, what about the impact of index building in parallel to the processing of “DATA LOAD INFILE” in general? We did not discuss whether we could accelerate the import time by omitting index building throughout the “LOAD DATA” import completely and creating the index instead afterwards! And we assumed that index creation during the import of a sequence of files would behave reasonably – i.e. linearly with the growing number of records. Actually, as further tests show the latter is not
always true and separating index creation from the execution of “LOAD DATA INFILE” is an important optimizing step! See a forthcoming article for a more detailed discussion of these interesting topics!

PHP/MySQL/Linux: File upload, database import and access right problems

Posted on by

On an (Apache) web server one should establish a policy regarding access rights to the files of hosted (virtual) domains. Especially with respect to files transferred by FTP or by uploads for a browser. It was an interesting experience for me that uploading files during an Ajax communication with a PHP program and moving them to target directories with PHP’s “move_uploaded_file” can collide with such policies and lead to unexpected results.

The problem

I recently tested Ajax controlled csv file transfers from a browser to a web server with a subsequent loading of the file contents to a database via PHP/MySQL. The database import was initiated by PHP by executing the SQL command “LOAD DATA INFILE” on the MySQL server. This chain of processes worked very well:

The uploaded csv file is moved from PHPs $_FILES superglobal array (which works as an input buffer for uploaded files) to a target directory on the web server by the means of the PHP function “move_uploaded_file”. My PHP program – the Ajax counterpart on the server – afterwards triggers a special MySQL loader procedure via the “LOAD DATA INFILE” command. MySQL then loads the data with very high speed into a specified database table. It is clear that the import requires sufficient database and table access rights which have to be specified by the PHP program when opening the database connection via one of PHP’s MySQL interfaces.

The overall success of the upload and database import sequence changed, however, in a surprising way when I wanted to transfer my (rather big) files in a zip-compressed form from the browser to the web server.

So I compressed my original csv file into a zip container file. This zip file was transferred to the server by using the same a web site formular and Ajax controls as before. On the server my PHP program went through the following steps to make the contents of the zip container available for further processing (as the import into the database) :

  • Step 1: I used “unlink” to delete any existing files in the target directory.
  • Step 2: I used “move_uploaded_file” to save the zip file into the usual target directory.
  • Step 3: I used the “ZipArchive” class and its methods from PHP to uncompress the zip-file content within the target directory.

Unfortunately, the “LOAD DATA INFILE” command failed under these conditions.

However, everything still worked well when I transferred uncompressed files. And even more astonishing:
style=”margin-left:20px;”>When I first uploaded the uncompressed version and then tried the same with the zip-version BUT omitted Step 1 above (i.e. did not delete the existing file in the target directory) “LOAD DATA” also worked perfectly.

It took me some time to find out what happened. The basic reason for this strange behavior was a peculiar way of how file ownership and access rights are handled by “move_uploaded_file” and by the method ZipArchive::extractTo. And in a wiggled way it was also due to some naivety on my side regarding my established access right policy on the server.

Note in addition: The failures took place although I used SGID and setfacl policies on the target directory (see below).

User right settings on my target directory

On some of my web servers – as the one used for the uploads – I restrict access rights to certain directories below the root directories of some virtual web domains to special user groups. One reason for this is the access control of different developer groups and FTP users. In addition I enforce automatic right and group settings for newly created files in these directories by ACL (setfacl) and SGID settings.

The Apache process owner becomes a member of the special group(s) owning these special directories. Which is natural as PHP has to work with the files.

Such an access policy was also established for the target directory of my file uploads. Let us say one of these special groups would be “devops” and our target directory would be “uploads”. Normally, when a user of “devops” (e.g. the Apache/PHP process)

  • creates a file in the directory “uploads”
  • or copies a file to the directory “uploads”

the file would get the group “devops” and “-rw-rw—-” rights – the latter due to my ACL settings.

What does “move_uploaded_file” do regarding file ownership and access rights ?

The first thing worthwhile to note is that the politics for what this PHP function does regarding ownership settings and access rights changed at some point in the past. It furthermore seems to depend on the fact whether the target directory resides on a different (mounted) file system. See the links given at the bottom of this file for more information.

Some years ago the right settings for the moved file in the target directory were “0600”. This obviously has changed :

  • The right settings of moved files today are “0644” (independent of file system questions).

However, what about the owner and the group of the moved file? Here “move_uploaded_file” seems to have it’s very own policy:

  • It sets the file owner to the owner of the Apache web server process (on my Opensuse Linux to “wwwrun“).
  • It sets the group always to “www” – and it does so
    independent of

    • whether the SGID sticky bit is set for the target directory or not (!) ,
    • whether the Apache web server process owner really is a member of “www” (!) ,
    • whether a file with the same name is already existing in the target directory with a maybe different group !

Funny, isn’t it? Test it out! I find the last 3 facts really surprising. They do not reflect a standard copy policy. As a result in my case the uploaded file always gets the following settings when saved to the file system by “move_uploaded_file”:

owner “wwwrun” and group “www” and the access rights “0644”.

So, after the transfer to the server, my target file ends up with being world readable!

What does ZipArchive do with respect to ownership and access rights ?

As far as I have tested “ZipArchive::extractTo” I dare say the following: It respects my sticky bit SGID and ACL settings. It behaves more or less like the Linux “cp” command would do.

So, when ZipArchive has done it’s extraction job the target file will have very different settings:

owner “wwwrun”, BUT group “devops” and the access rights “0660”.

However, this is only the case if the target file did not yet exist before the extraction !
If a file with the same name already existed in the target directory “ZipArchive::extractTo” respects the current owner/group and access rights settings (just as the cp command would have done!). Test it out!

The impact on MySQL’s “LOAD DATA INFILE …”

The MySQL process has its own owner – on my system “mysql”. When PHP issues the SQL command “LOAD DATA INFILE” by one of it’s MySQL interfaces the MySQL engine uses an internal procedure to access the (csv) file and loads it’s content into a database table. You may rightfully conclude that the opening of the file will be done by a process with “mysql” as the owner.

nSo, it becomes clear that not only the rights of the Apache/PHP process or database access rights are important in my scenario described above:

The MySQL (sub-) process itself must have access rights to the imported file!
 
But as we have learned: This is not automatically the case when the ZipArchive extraction has finalized – if “mysql” is not by accident or purpose a member of the group “devops”.

Now, take all the information given above together – and one understands the strange behavior:

  • When I uploaded the uncompressed file it got rights such that it was world readable and it’s contents could therefore be accessed by “mysql”. That guaranteed that “UPLOAD DATA INFILE” worked in the first standard scenario without a zip file.
  • If the target file is not deleted and exists before a zip extraction process it will be rewritten without a change of it’s properties. That makes the “LOAD DATA INFILE” work also after a zip extraction as long as we do not delete a previously existing target file from a standard upload with the same name and world readability.
  • However, in case of an emptied target directory the extraction process respects the SGID and ACL settings – and then “mysql” has no read access right for the file!

A conclusion is that I had stumbled into a problem which I had partially created by myself:

I had established a policy for group and right settings which was respected when extracting zipped files. This policy collided with required file access rights for the MySQL processes. Stupid me! Should have taken that into account !

Accidentally, my politics was not respected when uploading and handling standard files directly without encapsulating it in a zip container. Due to the failure of my policy and the disregard of special right settings by “move_uploaded_file” the subsequent MySQL process could use “LOAD UPLOAD INFILE”. Not what I wanted or had expected – but the policy violation almost went undetected due to the overall success.

Conclusions

I hope that the points discussed in this article have made it clear that the access rights of uploaded and/or zip-extracted files are something to take good care of on a Linux web server:

Do not rely on any expectations regarding of how PHP functions and objects may handle user/group ownerships and access rights! In case of file uploads (with and without zip containers) test it out, check the behavior of your programs and set the rights with available PHP file handling functionality explicitly to what to want to achieve.

The consequences of the peculiar rights settings of “move_uploaded_file” and it’s ignorance of SGID/ACL policies must be taken into account. Other PHP routines may handle access rights of copied/moved/extracted files differently than “move_uploaded_file”. Furthermore, if you want to load data from uploaded (csv) files into the database take into account that the “mysql” user needs read access rights for your file.

Solutions for my scenario with zip files

Let us assume that I follow my advice above and set the rights for files saved by “move_uploaded_file” explicitly, namely such that my policy with the group “devops” and the “0660” rights is respected. Than – without further measures – no uploaded file could be handled by the MySQL “UPLOAD DATA INFILE” mechanism. In my special case one could think about several possible solutions. I just name 3 of them:

Solution 1: If – for some reasons – you do not even temporarily want to break your SGID/ACL policies for the uploaded and extracted file in your target directory, you could make “mysql” a member of your special group (here “devops”). Note that this may also have some security
implications you should consider carefully.

Solution 2: You may set up another target directory with special access rights only to “mysql” and “wwwrun” by defining a proper group. This directory would only be used to manage the data load into the database. So, copy or move your uploaded/extracted file there, set proper rights if necessary and then start the “UPLOAD DATA” process for this target file. And clean the special directory afterwards – e.g. by moving your upload file elsewhere (for example to an archive).

Solution 3: You may change the access rights and/or the group of the file temporarily by the PHP program before initiating the MySQL “LOAD DATA” procedure. You may reset rights after the successful database import or you may even delete or move the file to another directory (by rename) – and adjust its rights there to whatever you want.

Whatever you do – continue having fun with Ajax and PHP controlled file uploads. I shall come back to this interesting subject in further articles.

Links

New access right settings of move_uploaded_file
https://blog.tigertech.net/posts/php-upload-permissions/
Old access right settings by move_uploaded_file and ignorance of SGID sticky bit
http://de1.php.net/manual/en/function.move-uploaded-file.php#85149
cp and access rights (especially when a file already exists in the target directory)
http://superuser.com/questions/409226/linux-permissions-and-owner-being-preserved-with-cp
The use of SGID
http://www.library.yale.edu/ wsg/docs/ permissions/ sgid.htm