Difference between revisions of "Partitioning Cache Data"

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(Base Distribution)
(New Node Reshuffle)
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=== New Node Reshuffle ===
 
=== New Node Reshuffle ===
  
The original distribution algorithm can be used with a slight modification, only update rows in the adpn_peer_titles table when new node is up after shuffle. Assuming the new node is not considered a AU owner for existing AUs. Will reduce current AU burden on non-owner peers by maintaining a consistent node count per AU but replacing extant with new node.  
+
The original distribution algorithm can be used with a slight modification, only update rows in the '''adpn_peer_titles''' table when new node is up after shuffle. This approach is assuming the new node is not to be considered an AU owner for existing AUs.  
  
 
  <nowiki>string[] _peers = GetPeerArray();
 
  <nowiki>string[] _peers = GetPeerArray();

Revision as of 09:44, 8 August 2013

Why Partition

Partitioned Cost Reductions

Quorum in the network is 3. (This is down from the previous value of 4... find out why).

Any sort of partitioning strategy would need to be implemented at the titledb.xml level. Title AUs can be assigned to peers centrally, and each peer should receive a custom titledb.xml file. If LOCKSS is unwilling to support that then there is alternative using local parameters.

org.lockss.titleDbs = http://bpldb.bplonline.org/etc/adpn/titledb-local.xml

Implementing a 1 + 6 partitioning strategy can save 12% on average for each network node. 1 + 6 indicates AU owner + 6 additional network nodes. Adding 2 additional nodes to the network can decrease per node storage by an average of 30%. Adding 4 additional nodes and partitioning cache data can save per node storage on average of 41%. This means we could store up to 18 TB of data on 10.6 TB nodes.

Implementing a 1 + 5 which is 6 discrete nodes in the network (double quorum), base storage decrease is 25% with no additional nodes. 1 + 5 with 4 additional nodes achieves a staggering 50% on average per node storage reduction.

All nodes (default configuration)

			
au_host	AUCount DiskCost (TB)   AUCount   DiskCost (TB) 
ADAH	778	5.13            667
AUB	778	5.13
BPL	778	5.13
SHC	778	5.13
TROY	778	5.13
UAB	778	5.13
UAT	778	5.13
UNA	778	5.13
		

Does not include vacated publisher AUs (which is between 500 and 600 GB).

1 + 6

        1+6 No New Nodes          1+6 2 New Nodes             1+6 4 New Nodes		
au_host	AUCount	DiskCost cost     AUCount DiskCost cost	      AUCount DiskCost cost
ADAH	667	4.62	-9.97%	  519	  3.95	   -22.98%    503     2.61     -49.11%
AUB	676	3.85	-24.94%	  540	  3.23	   -37.09%    425     3.17     -38.13%
BPL	669	4.51	-12.15%	  522	  3.11	   -39.44%    405     2.48     -51.57%
SHC	666	4.49	-12.41%	  519	  3.23	   -36.96%    408     2.84     -44.66%  
TROY	666	4.3	-16.09%	  519	  3.28	   -36.09%    401     3.35     -34.68%
UAB	667	4.6	-10.26%	  519	  3.03	   -40.89%    413     2.51     -51.13%
UAT	767	5.09	-0.86%	  751	  4.91	   -4.38%     740     4.81     -6.23%
UNA	667	4.45	-13.23%	  519	  3.28	   -36.09%    357     2.98     -41.96%
ADAH2				  518	  3.95		      407     2.54	
AUB2				  519	  3.95		      501     3.26	
BPL2							      457     2.87	
UAT2							      428     2.49	

1 + 5

	1+5 No New Nodes	        1+5 4 New Nodes		
au_host	AUCount	DiskCost cost		AUCount	DiskCost cost
ADAH	581	3.85	 -24.87%	395	1.96	 -61.80%
AUB	539	3.99	 -22.12%	349	2.91	 -43.30%
BPL	537	3.73	 -27.13%	463	2.78	 -45.74%
SHC	590	3.38	 -34.07%	350	2.95	 -42.46%
TROY	555	3.81	 -25.71%	327	1.84	 -64.18%
UAB	572	3.7	 -27.87%	331	2.11	 -58.88%
UAT	757	4.93	 -3.68%		736	4.87	 -5.02%
UNA	536	3.35	 -34.56%	312	2.38	 -53.55%
ADAH2					332	2.07	
AUB2					328	2.53	
BPL2					367	2.54	
UAT2					377	1.84	

Partition Implementation

Title List

The title list is the most crucial component for any partitioning. With close to 1000 AUs management of partition responsibilities would be cumbersome at the node level.

<lockss-config>
 <property name="org.lockss.titleSet">
  <property name="Birmingham Public Library">
   <property name="name" value="All Birmingham Public Library AUs" />
   <property name="class" value="xpath" />
   <property name="xpath" value="[attributes/publisher='Birmingham Public Library']" />
  </property>
 </property> 
 <property name="org.lockss.title">
  <property name="BirminghamPublicLibraryBasePluginBirminghamPublicLibraryCartographyCollectionMaps000400000599">
   <property name="attributes.publisher" value="Birmingham Public Library" />
   <property name="journalTitle" value="Birmingham Public Library Cartography Collection" />
   <property name="type" value="journal" />
   <property name="title" value="Birmingham Public Library Cartography Collection: Maps (000400-000599)" />
   <property name="plugin" value="org.bplonline.adpn.BirminghamPublicLibraryBasePlugin" />
   <property name="param.1">
    <property name="key" value="base_url" />
    <property name="value" value="http://bpldb.bplonline.org/adpn/load/" />
   </property>
   <property name="param.2">
    <property name="key" value="group" />
    <property name="value" value="Cartography" />
   </property>
   <property name="param.3">
    <property name="key" value="collection" />
    <property name="value" value="000400-000599" />
   </property>
  </property>
 </property>
</lockss-config>


Comprehending LOCKSS Title List

Nested same name elements with different levels of nesting depth causes some difficulty in comprehension using standard tools. Standard deserialization techniques won't work because group 1 property element collection (org.lockss.titleSet) has different depth than group 2 property element collection (org.lockss.title).

protected void DeserializeXml() {
 using (FileStream _f = new FileStream(Server.MapPath(@"titledb.xml"), FileMode.Open, FileAccess.Read))
 {
  XmlReaderSettings _sets = new XmlReaderSettings();
  _sets.IgnoreWhitespace = true;
  _sets.ProhibitDtd = false;

  XmlReader _xml = XmlReader.Create(_f, _sets);
  XmlSerializer _xs = new XmlSerializer(typeof(LockssTitleDb));
  LockssTitleDb _titles = (LockssTitleDb)_xs.Deserialize(_xml);
 
  for (int i = 0; i < _titles.LockssTitleSet.Length; i++)
  {
    if (i % 2 == 0)
    {
      // first group property (has attribute name=org.lockss.titleSet)
      // titleSet displays publisher detail
      // this assumes group of 2 for each publisher and AU list
    } 
    else 
    {
      // second group property (has attribute name=org.lockss.title)
      // each iteration is a new AU for group 1 publisher
      for (int j = 0; j < _titles.LockssTitleSet[i].ChildNodes.Count; j++)
      {
        // property  name = normalized AU string
        // outer AU definition
        foreach (XmlNode _node in _titles.LockssTitleSet[i].ChildNodes[j].ChildNodes)
        {
          // loop through property tags          
        }
      }
    }
  }
  _f.close();
 }
...
[XmlRoot("lockss-config")]
public class LockssTitleDb 
{
    // cannot create a single interface for all nesting depths with a single name
    [XmlAnyElement()]
    public XmlElement[] LockssTitleSet;
}

Local Data Store

http://bpldb.bplonline.org/images/adpn/datastore.png

XML Generation

ExpertConfig option org.lockss.titlesDb does not examine content type. It only looks at the URL ending and string matches .xml, else it assumes it is a .txt configuration file. See BaseConfigFile.java constructor.

Title Distribution

Base Distribution

Using the local data store, au_ids are auto-incremented. After the initial distribution, new AU releases can be isolated by selecting from last known au_id. Row timestamp could also be used.

string[] _peers = GetPeerArray();
foreach (DataRow _row in _titles.Rows)
{
 Shuffle(_peers);

 int _counter = 0;
 bool _isPeer = IsPeer(_row["au_pub_id"].ToString());
 int _maxCount = _isPeer ? 5 : 6; 
 // not every publisher is a peer

 // insert 6 5
 foreach (string _peer in _peers)
 {
  if (_counter == _maxCount) break;
  if (_peer.Equals(_row["au_pub_id"].ToString())) continue;

  _connect.Command.Parameters.Clear();
  _connect.Command.CommandText = "INSERT INTO `adpn_peer_titles` (`peer_id`, `au_id`) VALUES (?,?)";
  _connect.Command.Parameters.AddWithValue("?", _peer);
  _connect.Command.Parameters.AddWithValue("?", _row["au_id"].ToString());
  _connect.Command.ExecuteNonQuery();
  _counter++;
 }

 if (_isPeer)
 {
  // insert 1
  _connect.Command.Parameters.Clear();
  _connect.Command.CommandText = "INSERT INTO `adpn_peer_titles` (`peer_id`, `au_id`) VALUES (?,?)";
  _connect.Command.Parameters.AddWithValue("?", _row["au_pub_id"].ToString());
  _connect.Command.Parameters.AddWithValue("?", _row["au_id"].ToString());
  _connect.Command.ExecuteNonQuery();
 }
}

A resultant table set could look like the following:

peer_id  count(*)  % burden
ADAH	  499	  55%
AUB	  549	  61%
BPL	  520	  57%
SHC	  504	  56%
SHC1	  490	  54%
TROY	  492	  54%
UAB	  532	  59%
UAT	  852	  94%
UAT1	  482	  53%
UNA	  510	  56%
		
905	Titles

New Node Reshuffle

The original distribution algorithm can be used with a slight modification, only update rows in the adpn_peer_titles table when new node is up after shuffle. This approach is assuming the new node is not to be considered an AU owner for existing AUs.

string[] _peers = GetPeerArray();
foreach (DataRow _row in _titles.Rows)
{
 Shuffle(_peers);

 int _counter = 0;
 bool _isPeer = IsPeer(_row["au_pub_id"].ToString());
 int _maxCount = _isPeer ? 5 : 6; 
 // not every publisher is a peer

 // insert 6 5
 foreach (string _peer in _peers)
 {
  if (_counter == _maxCount) break;
  if (_peer.Equals(_row["au_pub_id"].ToString())) continue;

  // ONLY modify existing AU map when new node is up 
  if (_peer.Equals(newNodePeerId))
  {
    _connect.Command.Parameters.Clear();
    _connect.Command.CommandText = @"
UPDATE `adpn_peer_titles` AS `a`, 
      (SELECT  `b2`.`peer_id` FROM `adpn_peer_titles` AS `b2`  
       WHERE `b2`.`au_id` = ?  AND `b2`.`peer_id` != ? ORDER BY RAND() LIMIT 1) AS `b` 
SET `a`.`peer_id` = ?
WHERE `a`.`peer_id` = `b`.`peer_id`
AND `a`.`au_id` = ? ";

    _connect.Command.Parameters.AddWithValue("?", _row["au_id"].ToString());
    _connect.Command.Parameters.AddWithValue("?", _row["au_pub_id"].ToString());
    _connect.Command.Parameters.AddWithValue("?", _peer);
    _connect.Command.Parameters.AddWithValue("?", _row["au_id"].ToString());
    _connect.Command.ExecuteNonQuery();
  }
 }
}


Using the original distribution and reshuffling for a new node resulted in :

peer_id  count(*)  % burden
ADAH	  456     50%
AUB	  492  	  54%
BPL	  467     52%
SHC	  456	  50%
SHC1	  438	  48%
TROY  	  445	  49%
UAB	  466	  51%
UAT	  845	  93%
UAT1	  441	  49%
UNA	  467	  52%
BPL1	  457	  50%

905 titles 

Dead Node Reshuffle

When a node is no longer a part of the network:

  1. Delete peer_id from apdn_peer_titles
  2. Select a list of peer_ids and au_ids where count is less than maxcount
  3. Insert a shuffled peer id where not in peer_ids select list


The entire method :

private void DeadNodeShuffle()
{
  _connect.Command.Parameters.Clear();
  _connect.Command.CommandText = @"SELECT `au_id` FROM `adpn_peer_titles` GROUP BY `au_id` HAVING COUNT(*) < 6";
  DataTable _titles = new DataTable();
  using (OdbcDataAdapter _adapter = new OdbcDataAdapter(_connect.Command))
  {
    _adapter.Fill(_titles);
  }

  foreach (DataRow _row in _titles.Rows)
  {
    _connect.Command.Parameters.Clear();
    _connect.Command.CommandText = @"INSERT INTO `adpn_peer_titles` (`peer_id`, `au_id`) 
      (SELECT `adpn_peers`.`peer_id`, (SELECT `au_id` FROM `au_titlelist` WHERE `au_id` = ?) AS 'au_id'
       FROM `adpn_peers` 
       WHERE `adpn_peers`.`active` = 'y'
       AND  `adpn_peers`.`peer_id` NOT IN (SELECT `peer_id` FROM `adpn_peer_titles` WHERE `au_id` = ?)       
       ORDER BY RAND() LIMIT 1)";
    _connect.Command.Parameters.AddWithValue("?", _row["au_id"].ToString());
    _connect.Command.Parameters.AddWithValue("?", _row["au_id"].ToString());
    _connect.Command.ExecuteNonQuery();
  }
}


Dropping a node (SHC1) and reshuffling AUs without full holdings results in the following table.

peer_id   count(*)   % burden
ADAH	  496	    55%
AUB	  528	    58%
BPL	  512	    57%
BPL1	  514	    57%
SHC	  500	    55%
TROY	  503	    56%
UAB	  520	    57%
UAT	  850	    94%
UAT1	  496	    55%
UNA	  511	    56%

905 titles