Archivematica 1.4 is a legacy release, and these documents are no longer being maintained.


This page contains information releavant to maintaining your Archivematica installation.

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Since version 0.9, Archivematica stores AIP file information, such as METS data, using Elasticsearch. This data can be searched from the Archival Storage area of the dashboard or can be interfaced with programmatically.

Programatic access to indexed AIP data

To access indexed AIP data using a custom script or application, find an Elasticsearch interface library for the programming language you’ve chosen to use. In Archivematica we use Python with the pyes library. In our developer documentation, we’ll outline the use of pyes to access AIP data, but any programming language/interface library, such as PHP and Elastica, should work.

Connecting to Elasticsearch

In this section we’ll run through an example of interfacing with Elasticsearch data using a Python script that leverages the pyes library.

The first step, when using pyes, is to require the module. The following code imports pyes functionality on a system on which Archivematica is installed.

import sys
from pyes import *

Next you’ll want to create a connection to Elasticsearch.

conn = ES('')

Full text searching

Once connected to Elasticsearch, you can perform searches. Below is the code needed to do a “wildcard” search for all AIP files indexed by Elasticsearch and retrieve the first 20 items. Instead of doing a “wildcard” search you could also supply keywords, such as a certain AIP UUID.

start_page     = 1
items_per_page = 20

q = StringQuery('*')

  results = conn.search_raw(
     start=start_page - 1,
   print 'Query error.'

Querying for specific data

While the “StringQuery” query type is good for broad searches, you may want to narrow a search down to a specific field of data to reduce false positives. Below is an example of searching documents, using “TermQuery”, matching criteria within specific data. As, by default, Elasticsearch stores term values in lowercase the term value searched for must also be lowercase.

import sys
import pyes

conn = pyes.ES('')

q = pyes.TermQuery("METS.amdSec.ns0:amdSec_list.@ID", "amdsec_8")

    results = conn.search_raw(query=q, indices='aips')
  print 'Query failed.'

Displaying search results

Now that you’ve performed a couple of searches, you can display some results. The below logic cycles through each hit in a results set, representing an AIP file, and prints the UUID of the AIP the file belongs in, the Elasticsearch document ID corresponding to the indexed file data, and the path of the file within the AIP.

if results:
    document_ids = []
    for item in results.hits.hits:
        aip = item._source
        print 'AIP ID: ' + aip['AIPUUID'] + ' / Document ID: ' + item._id
        print 'Filepath: ' + aip['filePath']

Fetching specific documents

If you want to get Elasticsearch data for a specific AIP file, you can use the Elasticsearch document ID. The above code populates the document_ids array and the below code uses this data, retrieving individual documents and extracting a specific item of data from each document.

for document_id in document_ids:
    data = conn.get(index_name, type_name, document_id)

    format = data['METS']['amdSec']['ns0:amdSec_list'][0]['ns0:techMD_list'][0]['ns0:mdWrap_list'][0]['ns0:xmlData_list'][0]['ns1:object_list'][0]['ns1:objectCharacteristics_list'][0]['ns1:format_list'][0]['ns1:formatDesignation_list'][0]['ns1:formatName']

    print 'Format for document ID ' + document_id + ' is ' + format

Augmenting documents

To add additional data to an Elasticsearch document, you’ll need the document ID. The following code shows an Elasticsearch query being used to find a document and update it with additional data. Note that the name of the data field being added, “__public”, is prefixed with two underscores. This practice prevents the accidental overwriting of system or Archivematica-specific data. System data is prefixed with a single underscore.

import sys
import pyes

conn = pyes.ES('')

q = pyes.TermQuery("METS.amdSec.ns0:amdSec_list.@ID", "amdsec_8")

results = conn.search_raw(query=q, indices='aips')

  if results:
    for item in results.hits.hits:
        print 'Updating ID: ' + item['_id']

        document = item['_source']
        document['__public'] = 'yes'
        conn.index(document, 'aips', 'aip', item['_id'])
  print 'Query failed.'

Delete Elasticsearch index through GUI

To help with Elasticsearch development, Archivematica comes with a plugin for Elasticsearch, called Elasticsearch Head, that provides a web application forcbrowsing and administering Elasticsearch data. It can be accessed at

Elasticsearch Head will allow you to delete an index, if need be.

Deleting an Elasticsearch index from Elasticsearch Head.

Delete Elasticsearch index programmatically

If, for whatever reason, you need to delete an Elasticsearch index programmatically, this can be done with pyes using the following code.

import sys
from pyes import *
conn = ES('')

    print "Error deleting index or index already deleted."

Rebuilding the AIP index

To rebuild the ElasticSearch AIP index enter the following to find the location of the rebuilding script:

locate rebuild-elasticsearch-aip-index-from-files

Copy the location of the script then enter the following to perform the rebuild (substituting “/your/script/location/rebuild-elasticsearch-aip-index- from-files” with the location of the script):

/your/script/location/rebuild-elasticsearch-aip-index-from-files <location of your AIP store>

Rebuilding the transfer index

Similarly, to rebuild the ElasticSearch transfer data index enter the following to find the location of the rebuilding script:

locate rebuild-elasticsearch-transfer-index-from-files

Copy the location of the script then enter the following to perform the rebuild (substituting “/your/script/location/rebuild-elasticsearch-transfer- index-from-files” with the location of the script):

/your/script/location/rebuild-elasticsearch-transfer-index-from-files <location of your AIP store>

See also

Elasticsearch troubleshooting help from AtoM documentation.

Data back-up

In Archivematica there are three types of data you’ll likely want to back up:

  • Filesystem (particularly your storage directories)
  • MySQL
  • ElasticSearch

MySQL is used to store short-term processing data. You can back up the MySQL database by using the following command:

mysqldump -u <your username> -p<your password> -c MCP > <filename of backup>

ElasticSearch is used to store long-term data. Instructions and scripts for backing up and restoring ElasticSearch are available here.


How to restart the Archivematica services


sudo stop archivematica-mcp-server
sudo stop archivematica-mcp-client
sudo /etc/init.d/apache2 stop
sudo /etc/init.d/gearman-job-server stop
sudo stop mysql
sudo /etc/init.d/elasticsearch stop


sudo /etc/init.d/elasticsearch start
sudo start mysql
sudo /etc/init.d/gearman-job-server start
sudo /etc/init.d/apache2 start
sudo start archivematica-mcp-server
sudo start archivematica-mcp-client

Error stack trace

“I am getting a white error page in the Dashboard. How can I find out what the error is?”

Seeing an full error stack trace for the Dashboard requires knowing how to edit a configuration file from the command line.

  1. SSH to the Archivematica server
  2. With your preferred text editor, open the dashboard settings file (vim shown in example; Note that you must use sudo to edit this file):
sudo vim /usr/share/archivematica/dashboard/settings/
  1. Change the “DEBUG” flag from “False” to “True”
DEBUG = True
  1. Save the file
  2. Restart Apache
sudo apache2ctl restart
  1. Reload the dashboard page reporting the error in your browse
  2. Debug or report error
  3. Restore DEBUG to False and restart Apache to turn error reporting off again

Browser compatability

Archivematica has been tested most extensively with Firefox and Chrome. There are known issues with Internet Explorer 11 which result in an inability to start transfers in the dashboard (Issue 7246)

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