The transmart-data collection of scripts is not anymore the recommended way to create a TranSMART database. Please use the Liquibase scripts in transmart-schemas instead.
This repository is a set of make files and scripts for:
- creating the tranSMART database and all its objects;
- populating the database with the bare minimum data necessary to start tranSMART and use the ETL scripts;
- generating the SQL scripts and dependency metadata necessary for the two points above, from a clean database;
- fixing permissions and tablespace assignments;
- importing some example data - these are intended for development, the targets are not robust enough to be generally applicable;
- running the Solr cores for Faceted Search and the Sample Explorer;
- generating configuration files for tranSMART.
The current schema is the one necessary to support the 17.1 versions of tranSMART for Oracle and Postgres
Some database migration scripts are available to migrate from previous versions of the tranSMART database schema.
A script is available that can compare dumps from two databases,
intended for an installed copy to be compared to the latest
transmart-data
.
The following are required:
- PostgreSQL (9.5 recommended, 10.2 is known to not work currently) or Oracle database server
- GNU make
- PostgreSQL client utilities (
psql
,psql_dump
, version matching your database), if you use PostgreSQL - curl
- php (>= 5.4)
- tar with support for the -J switch (GNU tar only?)
- An up-to-date checkout of the
tranSMART-ETL
repository. Revision e712fcd7 is necessary for Faceted Search support (ETL only) - Groovy (>= 2.1 and <= 2.4). Can be installed with
make -C env groovy
and updating thePATH
(Oracle and some secondary functionality only) - Kettle (ETL only)
- rsync (Solr only)
- Java environment (JDK 7 or 8)
- build-essentials tools and gcc-fortran (building R from source only)
If you are using Ubuntu and you intend to use PostgreSQL, you should be able to install all these dependencies by running
sudo make -C env ubuntu_deps_root
make -C env ubuntu_deps_regular
which will also prepare some directories for the tablespaces and assign them the correct ownership.
Start with copying the vars.sample
file, editing it and sourcing it in:
cp vars.sample vars
vim vars
# edit file and save...
. ./vars
If you ran make -C env ubuntu_deps_regular
, you will have a vars
file
created for you. You can skip the previous step and do only:
. ./vars
The several options are fairly self-explanatory.
These scripts were tested with PostgreSQL version 9.5. PostgreSQL version 10.2 is known to not work.
The configured PostgreSQL user must be a database superuser. You can connect to
PostgreSQL with UNIX sockets by specifying the parent directory of the socket
in PGHOST
. In that case, localhost
will be used in the situation where UNIX
sockets are not supported, such as for JDBC connections.
The variable $TABLESPACES
is the parent directory for where the tablespaces
will be created in the PostgreSQL server's filesystem.
The database creation target assumes the PostgreSQL server runs on the same
machine. The target does not attempt to create the tablespaces if they already
exist, but it will nevertheless attempt to create the directories where it
thinks the tablespaces should be according to $TABLESPACES
.
Note that if the tablespaces directories do not already exist or are not assigned the correct owner (i.e., the user PostgreSQL runs as), then the install target will run into problems when attempting to create the tablespaces. If the user PostgreSQL runs as and the user running the targets are not the same AND the tablespace directories do not already exist, then manual intervention is necessary for creating all the tablespace directories and assigning them the correct owner.
This install script assumes SYSDBA privileges in order to create the users that the application will use.
You will need to create the tablespaces TRANSMART
and INDX
(sic) manually
before creating the database. The install script can try to do this
automatically if you set ORACLE_MANAGE_TABLESPACES=1
, but in that case you
will need to set ORACLE_TABLESPACES_DIR
to a directory name (on the database
server) where Oracle can automatically create the files to store the new
tablespaces and other database objects.
Transmart requires the 'Partitioning' feature of the database, so make sure that feature has not been disabled in the Oracle database. 'Partitioning' is (as of Oracle Database 12) only supported in Oracle Database Enterprise Edition, so Transmart will not run against a lower level edition of Oracle.
make postgres_drop
make oracle_drop
make postgres
make oracle
For PostgreSQL, you can skip the tablespace assignments, which are not really
important for development, by setting the environment variable
skip_fix_tablespaces
to any non-empty value:
skip_fix_tablespaces=1 make -j4 postgres
The Oracle version, on the other hand, does not manage tablespaces by default.
For forcing, use ORACLE_MANAGE_TABLESPACES
:
ORACLE_MANAGE_TABLESPACES=1 make -j4 oracle
For PostgreSQL, there's a simple script in data/postgres/set_password.sh
for
changing users' passwords. If you're using Oracle, you can still use part of it
to generate the hashes.
make postgres_test
make oracle_test
These can be done with the targets fix_permissions
, fix_owners
and
fix_tablespaces
, under ddl/postgres/META
. Example:
make -C ddl/postgres/META fix_permissions
This project can be used to download public or privately available datasets and load these into the database. Typically, but not always, this loading involves using Kettle jobs (with kitchen) available in an existing checkout of (tranSMART-ETL)[ts-etl] .
Data fetching is done through a system of feeds that publish several data sets. A data set is a combination of meta data and, for most data types, properly formatted data files. The most important meta data, the meta data that uniquely identifies the data set, are the study name and the tranSMART data type (clinical data, mRNA data, etc.). A feed is an object that provides a list of triplets consisting of study name, data type and location of a tarball containing the data and full metadata for the dataset.
The tarballs must follow the following rules:
- they must be compressed with xz;
- their filename must be in the form
<study name>_<data type>.tar.xz
; - they must have a file named
<data type>.params
in their root; - most data types require, under the root, a directory named
<data type>
, under which the data files should be located.
For instance, a clinical data tarball could contain the following tree:
.
|-- clinical
| |-- E-GEOD-8581.sdrf-rewrite.txt
| |-- E-GEOD-8581_columns.txt
| `-- E-GEOD-8581_wordmap.txt
`-- clinical.params
Feeds are listed in samples/studies/public-feeds
and, optionally, in a
git-ignored private-feeds
, in the root of the project. Each file contains a
list of feeds in the following format:
<feed type> <type-specific feed location data>
The two supported feed types right now are http-index
and ftp-flat
.
Examples:
http-index http://studies.thehyve.net/datasets_index
ftp-flat ftp://studies.thehyve.net/
The initial public-feeds download uses a TranSMART Foundation
host. Changing the URL in samples/studies/public-feeds
will use an
alternative source for all data downloads.
The type http-index
points to a plain text file that lists the available
datasets in the format <study> <type> <tarball relative path>
. Example:
Cell-line acgh Cell-line/Cell-line_acgh.tar.xz
Cell-line clinical Cell-line/Cell-line_clinical.tar.xz
The type ftp-flat
points to an FTP directory that should store, directly
underneath it, all the dataset tarballs.
The list of datasets is automatically downloaded when needed, but it is not automatically updated. To update it, run at the root:
make update_datasets
If a data set (study/type combination) is repeated, transmart-data will assume it is the same data and will download randomly from any source. If any download fails, another source will be tried until there is none left.
When developing the data sets, the best course of action is to place the files
directly under samples/studies/<study name>
. The contents of this directory
should include the contents of the tarball. When finished, the tarball can be
created with (running from the root of the project):
tar -C samples/studies/<study name> \
-cJf <study name>_<data type>.tar.xz <data type>.params <data type>
Data sets can be loaded by running a target named load_<data type>_<study name>
in either samples/oracle
or samples/postgresql
, as appropriate.
For instance:
make -C samples/{oracle,postgres} load_clinical_GSE8581
make -C samples/{oracle,postgres} load_ref_annotation_GSE8581
make -C samples/{oracle,postgres} load_expression_GSE8581
Do not forget to update your Solr index, if your setup requires it to be triggered manually.
After loading new data you might need to update materialized views. e.g. BIOMART.BIO_MARKER_CORREL_MV
You could do this with help of the following command:
make -C data/oracle refresh_mvs
The loading scripts use the -e option from the function readlink. This is a function that is not in the readlink that is installed on Mac OSX, to bypass this problem you are required to install greadlink (stands for GNU readlink). After installing greadlink edit ~/transmart-data/samples/postgres/process_params.inc and on change readlink to greadlink (line 20). Save the changes and the upload should work.
To start a Solr instance with one core for Faceted Search and another for the sample explorer:
make -C solr start
Once it is running, you can run full imports with:
make -C solr browse_full_import rwg_full_import sample_full_import
The Faceted Search core also supports delta imports:
make -C solr rwg_delta_import
Due to different functionality in tranSMART versions targeting each RDBMS, there's a separate Solr core for Oracle:
ORACLE=1 make -C solr start
ORACLE=1 make -C solr sanofi_full_import
Document (e.g. PDFs) reindexing has a special procedure.
You can install a recent version of R from your distro and run the R scripts
inside the R
directory to install the required packages. You can also build R
from source:
make -C R -j8 root/bin/R
make -C R install_packages
Beware that if you run install_packages
with concurrency some packages will
fail building and you may have to run the command again.
There is an init script and a systemd unit available to run Rserve. It has to be run as the same user as the tranSMART web application:
TRANSMART_USER=<tomcat user> make -C R install_rserve_init
update-rc.d rserve defaults 85 # to enable the service
This is applicable to the Debian-family distributions. See also the
install_rserve_unit
target for systemd based distros.
The default schema permissions are set in
ddl/postgres/META/default_permissions.php
. Tables where biomart_user
should be able to write are defined in
ddl/postgres/META/biomart_user_write.tsv
. Extra miscellaneous permissions are
defined in ddl/postgres/META/misc_permissions.tsv
.
Ownership information can only be added by editing an array in
ddl/postgres/META/assign_owners.sql
.
For development, the default configuration should be sufficient. For production, the configuration may have to be changed, but even if it doesn't, some directories described in the configuration will need to be created.
If you need to change configuration parameters, you can change the files in
~/.grails/transmartConfig
that are created by the install
target, but they
will be overwritten (after being backed up) the next time you install the
configuration again using the same target. Therefore, it is preferable to copy
config/Config-extra.php.sample
into config/Config-extra.php
and edit the new
file. In this file, you can edit two blocks of text which will be inserted into
two different points in the configuration template, allowing you override any
option that the configuration template sets.
To install the configuration files into $TSUSER_HOME/.grails/transmartConfig
,
($TSUSER_HOME
is one of the environment variables defined in the vars
file)
run:
make -C config install
This part still needs some work, but it goes more or less like this:
- Start with a clean database. Preferably load if from scratch with
make postgres
and make only the necessary changes on top of this. - Go to
ddl/postgres
. - Run
make clean
. - Manually delete the subdirectories with the names of the schemas that you changed.
- Run
make <schema name>_files
for every schema whose directory you deleted. - (If you want to regenerate all the schemas, you can replace the last three
steps with
make clean_all dump files_all
). - If you have data changes, go to
data/postgres
. If you need to dump data from a table no data was being dumped from before, add that table to one of the<schema name>_list
files. - Run
make dump
. You can runmake clean_dumps
to delete all the dumps, which might be useful e.g. if you deleted tables from the lists of tables that should be dumped. - If you changed global objects like roles (unlikely), look at the makefile on
ddl/postgresql/GLOBAL
for more information. - Make sure you test the changes by recreating the database.
Note that pg_dump does not output the data in any specific order; after a table is modified, it will dump the old data out of order. This results in a larger changeset than necessary and obfuscates the actual changes made to the table's data. A target is included that attempts to remedy this problem by changing the modified files so that the old rows are kept at the top of the modified file in the same order (but with the removed rows left out):
make -C data/common minimize_diffs
Use this target after modifying the tsv files but before checking them into the
git index with git add
. The procedure compares the version in the working
directory with the version in the git index.
To update the plugin module params from the database (a doubtful course of
action; better to edit the files in
data/common/searchapp/plugin_modules_params/
), then do:
cd data/postgres
make searchapp/Makefile
make -C searchapp dump_plugin_module
make -C ../common/searchapp/plugin_modules_params process_dump
For Oracle, only schema dumping and loading is supported:
make -C ddl/oracle dump
make -C ddl/oracle load
A separate branch exists that supports loading data from the TSV files dumped by PostgreSQL.