Jan
23
2019
--

MongoDB Replica Set Scenarios and Internals – Part II (Elections)

mongodb node election to primary

In this blog post, we will walk through the internals of the election process in MongoDB®, following on from a previous post on the internals of the replica set. You can read Part 1 here.

For this post, I am refer to the same configurations we discussed before.

Elections: As the term suggests, in MongoDB there is a freedom to “vote”: individual nodes of the cluster can vote and select their primary member for that replica set cluster.

Why Elections? MongoDB maintains high availability through this process.

When do elections take place?

  1. When the node does not found a primary node within the election timeout limit. By default this value is 10s, and from MongoDB version 3.2 this can be changed according to your needs.  The parameter to set this value is
    settings.electionTimeoutMillis

      and can be seen in the logs as:

settings: { chainingAllowed: true, heartbeatIntervalMillis: 2000, heartbeatTimeoutSecs: 10, electionTimeoutMillis: 10000, catchUpTimeoutMillis: 60000, getLastErrorModes: {}, getLastErrorDefaults: { w: 1, wtimeout: 0 }, replicaSetId: ObjectId('5ba8ed10d4fddccfedeb7492') } }

From the mongo shell, the value for the

electionTimeoutMillis

  can be found in replica set configuration as:

rplint:SECONDARY> rs.conf()
{
	"_id" : "rplint",
	"version" : 3,
	"protocolVersion" : NumberLong(1),
	"members" : [
		{
			"_id" : 0,
			"host" : "m103:25001",
			"arbiterOnly" : false,
			"buildIndexes" : true,
			"hidden" : false,
			"priority" : 1,
			"tags" : {
			},
			"slaveDelay" : NumberLong(0),
			"votes" : 1
		},
		{
			"_id" : 1,
			"host" : "192.168.103.100:25002",
			"arbiterOnly" : false,
			"buildIndexes" : true,
			"hidden" : false,
			"priority" : 1,
			"tags" : {
			},
			"slaveDelay" : NumberLong(0),
			"votes" : 1
		},
		{
			"_id" : 2,
			"host" : "192.168.103.100:25003",
			"arbiterOnly" : false,
			"buildIndexes" : true,
			"hidden" : false,
			"priority" : 1,
			"tags" : {
			},
			"slaveDelay" : NumberLong(0),
			"votes" : 1
		}
	],
	"settings" : {
		"chainingAllowed" : true,
		"heartbeatIntervalMillis" : 2000,
		"heartbeatTimeoutSecs" : 10,
		"electionTimeoutMillis" : 10000,
		"catchUpTimeoutMillis" : 60000,
		"getLastErrorModes" : {
		},
		"getLastErrorDefaults" : {
			"w" : 1,
			"wtimeout" : 0
		},
		"replicaSetId" : ObjectId("5c20ff87272eff3a5e28573f")
	}
}

More precisely the value for

electionTimeoutMillis

  can be found at:

rplint:SECONDARY> rs.conf().settings.electionTimeoutMillis
10000

2.  If the priority of the existing primary node is being taken over by another node. For example, during planned maintenance using replica set configuration settings. The priority of the member node can be changed as explained here

The priority of all three members can be seen from the replica set configuration like this:

rplint:SECONDARY> rs.conf().members[0].priority
1
rplint:SECONDARY>
rplint:SECONDARY>
rplint:SECONDARY> rs.conf().members[2].priority
1
rplint:SECONDARY> rs.conf().members[1].priority
1

How do elections work in a MongoDB replica set cluster?

Before real elections, the node runs a dry election. Dry election? Yes, the node first runs dry elections, and if the node wins a dry election, then an actual election begins. Here’s how:

  1. Candidate node asks every node if another node would vote for it through
    replSetRequestVotes

     , without increasing the term itself.

  2. Primary node steps down if it finds a candidate node term higher than itself. Otherwise the dry election fails, and the replica set continues to run as is did before.
  3. If the dry election succeeds, then an actual election begins.
  4. For the real election, the node increments its term and then votes for itself.
  5. VoterRequester sends
    replSetRequestVotes

     command through ScatterGatherRunner and then each node responds back with their vote.

  6. The candidate that receives votes from the most nodes wins the election.
  7. Once the candidate wins, it transits to primary node. Through heartbeats it sends a notification to all other nodes.
  8. Then the candidate node checks if it needs to catch up from the former primary node.
  9. The node that receives the 
    replSetRequestVotes

     command checks its own term and then votes, but only after ReplicationCoordinator receives confirmation from TopologyCoordinator

  10. The TopologyCoordinator grants the vote after following considerations:
    1. Config version must be matched,
    2. Replica set name must be matched
    3. An arbiter voter must not see any healthy primary of greater or equal priority.

An example

A primary (port:25002) Transition to secondary after receiving the

rs.stepDown()

  command.

2019-01-03T03:05:29.972+0000 I COMMAND  [conn124] Attempting to step down in response to replSetStepDown command
2019-01-03T03:05:29.976+0000 I REPL     [conn124] transition to SECONDARY
driver: { name: "NetworkInterfaceASIO-Replication", version: "3.4.15" }, os: { type: "Linux", name: "Ubuntu", architecture: "x86_64", version: "14.04" } }
2019-01-03T03:05:40.874+0000 I REPL     [ReplicationExecutor] Member m103:25001 is now in state PRIMARY
2019-01-03T03:05:41.459+0000 I REPL     [rsBackgroundSync] sync source candidate: m103:25001
2019-01-03T03:05:41.459+0000 I ASIO     [NetworkInterfaceASIO-RS-0] Connecting to m103:25001
2019-01-03T03:05:41.460+0000 I ASIO     [NetworkInterfaceASIO-RS-0] Successfully connected to m103:25001, took 1ms (1 connections now open to m103:25001)
2019-01-03T03:05:41.461+0000 I ASIO     [NetworkInterfaceASIO-RS-0] Connecting to m103:25001
2019-01-03T03:05:41.462+0000 I ASIO     [NetworkInterfaceASIO-RS-0] Successfully connected to m103:25001, took 1ms (2 connections now open to m103:25001)

Dry election at candidate node (port:25001) and success: no primary found.

2019-01-03T03:05:31.498+0000 I REPL     [rsBackgroundSync] could not find member to sync from
2019-01-03T03:05:36.493+0000 I REPL     [SyncSourceFeedback] SyncSourceFeedback error sending update to 192.168.103.100:25002: InvalidSyncSource: Sync source was cleared. Was 192.168.103.100:25002
2019-01-03T03:05:39.390+0000 I REPL     [ReplicationExecutor] Starting an election, since we've seen no PRIMARY in the past 10000ms
2019-01-03T03:05:39.390+0000 I REPL     [ReplicationExecutor] conducting a dry run election to see if we could be elected. current term: 35
2019-01-03T03:05:39.391+0000 I REPL     [ReplicationExecutor] VoteRequester(term 35 dry run) received a yes vote from 192.168.103.100:25002; response message: { term: 35, voteGranted: true, reason: "", ok: 1.0 }

Dry election succeeds and increments term by 1 (here the term was 35 and is incremented to 36). It transitions to primary and enters catchup mode.

2019-01-03T03:05:39.391+0000 I REPL [ReplicationExecutor] dry election run succeeded, running for election in term 36
2019-01-03T03:05:39.394+0000 I REPL [ReplicationExecutor] VoteRequester(term 36) received a yes vote from 192.168.103.100:25003; response message: { term: 36, voteGranted: true, reason: "", ok: 1.0 }
2019-01-03T03:05:39.395+0000 I REPL [ReplicationExecutor] election succeeded, assuming primary role in term 36
2019-01-03T03:05:39.395+0000 I REPL [ReplicationExecutor] transition to PRIMARY
2019-01-03T03:05:39.395+0000 I REPL [ReplicationExecutor] Entering primary catch-up mode.

Other nodes also receive information about the new primary.

2019-01-03T03:05:31.498+0000 I REPL [rsBackgroundSync] could not find member to sync from
2019-01-03T03:05:36.493+0000 I REPL [SyncSourceFeedback] SyncSourceFeedback error sending update to 192.168.103.100:25002: InvalidSyncSource: Sync source was cleared. Was 192.168.103.100:25002
2019-01-03T03:05:41.499+0000 I REPL [ReplicationExecutor] Member m103:25001 is now in state PRIMARY

This is how MongoDB is able to maintain high availability by electing primary node from the replica set clusters in the case of existing primary node failures.


Photo by Daria Shevtsova from Pexels

Oct
10
2018
--

MongoDB Replica set Scenarios and Internals

MongoDB replica sets replication internals r

MongoDB replica sets replication internals rThe MongoDB® replica set is a group of nodes with one set as the primary node, and all other nodes set as secondary nodes. Only the primary node accepts “write” operations, while other nodes can only serve “read” operations according to the read preferences defined. In this blog post, we’ll focus on some MongoDB replica set scenarios, and take a look at the internals.

Example configuration

We will refer to a three node replica set that includes one primary node and two secondary nodes running as:

"members" : [
{
"_id" : 0,
"name" : "192.168.103.100:25001",
"health" : 1,
"state" : 1,
"stateStr" : "PRIMARY",
"uptime" : 3533,
"optime" : {
"ts" : Timestamp(1537800584, 1),
"t" : NumberLong(1)
},
"optimeDate" : ISODate("2018-09-24T14:49:44Z"),
"electionTime" : Timestamp(1537797392, 2),
"electionDate" : ISODate("2018-09-24T13:56:32Z"),
"configVersion" : 3,
"self" : true
},
{
"_id" : 1,
"name" : "192.168.103.100:25002",
"health" : 1,
"state" : 2,
"stateStr" : "SECONDARY",
"uptime" : 3063,
"optime" : {
"ts" : Timestamp(1537800584, 1),
"t" : NumberLong(1)
},
"optimeDurable" : {
"ts" : Timestamp(1537800584, 1),
"t" : NumberLong(1)
},
"optimeDate" : ISODate("2018-09-24T14:49:44Z"),
"optimeDurableDate" : ISODate("2018-09-24T14:49:44Z"),
"lastHeartbeat" : ISODate("2018-09-24T14:49:45.539Z"),
"lastHeartbeatRecv" : ISODate("2018-09-24T14:49:44.664Z"),
"pingMs" : NumberLong(0),
"syncingTo" : "192.168.103.100:25001",
"configVersion" : 3
},
{
"_id" : 2,
"name" : "192.168.103.100:25003",
"health" : 1,
"state" : 2,
"stateStr" : "SECONDARY",
"uptime" : 2979,
"optime" : {
"ts" : Timestamp(1537800584, 1),
"t" : NumberLong(1)
},
"optimeDurable" : {
"ts" : Timestamp(1537800584, 1),
"t" : NumberLong(1)
},
"optimeDate" : ISODate("2018-09-24T14:49:44Z"),
"optimeDurableDate" : ISODate("2018-09-24T14:49:44Z"),
"lastHeartbeat" : ISODate("2018-09-24T14:49:45.539Z"),
"lastHeartbeatRecv" : ISODate("2018-09-24T14:49:44.989Z"),
"pingMs" : NumberLong(0),
"syncingTo" : "192.168.103.100:25002",
"configVersion" : 3
}

Here, the primary is running on port 25001, and the two secondaries are running on ports 25002 and 25003 on the same host.

Secondary nodes can only sync from Primary?

No, it’s not mandatory. Each secondary can replicate data from the primary or any other secondary to the node that is syncing. This term is also known as chaining, and by default, this is enabled.

In the above replica set, you can see that secondary node

"_id":2 

  is syncing from another secondary node

"_id":1

   as

"syncingTo" : "192.168.103.100:25002" 

This can also be found in the logs as here the parameter

chainingAllowed :true

   is the default setting.

settings: { chainingAllowed: true, heartbeatIntervalMillis: 2000, heartbeatTimeoutSecs: 10, electionTimeoutMillis: 10000, catchUpTimeoutMillis: 60000, getLastErrorModes: {}, getLastErrorDefaults: { w: 1, wtimeout: 0 }, replicaSetId: ObjectId('5ba8ed10d4fddccfedeb7492') } }

Chaining?

That means that a secondary member node is able to replicate from another secondary member node instead of from the primary node. This helps to reduce the load from the primary. If the replication lag is not tolerable, then chaining could be disabled.

For more details about chaining and the steps to disable it please refer to my earlier blog post here.

Ok, then how does the secondary node select the source to sync from?

If Chaining is False

When chaining is explicitly set to be false, then the secondary node will sync from the primary node only or could be overridden temporarily.

If Chaining is True

  • Before choosing any sync node, TopologyCoordinator performs validations like:
    • Whether chaining is set to true or false.
    • If that particular node is part of the current replica set configurations.
    • Identify the node ahead with oplog with the lowest ping time.
    • The source code that includes validation is here.
  • Once the validation is done, SyncSourceSelector relies on SyncSourceResolver which contains the result and details for the new sync source
  • To get the details and response, SyncSourceResolver coordinates with ReplicationCoordinator
  • This ReplicationCoordinator is responsible for the replication, and co-ordinates with TopologyCoordinator
  • The TopologyCoordinator is responsible for topology of the cluster. It finds the primary oplog time and checks for the maxSyncSourceLagSecs
  • It will reject the source to sync from if the maxSyncSourceLagSecs  is greater than the newest oplog entry. The code for this can be found here
  • If the criteria for the source selection is not fulfilled, then BackgroundSync thread waits and restarts the whole process again to get the sync source.

Example for “unable to find a member to sync from” then, in the next attempt, finding a candidate to sync from

This can be found in the log like this. On receiving the message from rsBackgroundSync thread

could not find member to sync from

, the whole internal process restarts and finds a member to sync from i.e.

sync source candidate: 192.168.103.100:25001

, which means it is now syncing from node 192.168.103.100 running on port 25001.

2018-09-24T13:58:43.197+0000 I REPL     [rsSync] transition to RECOVERING
2018-09-24T13:58:43.198+0000 I REPL     [rsBackgroundSync] could not find member to sync from
2018-09-24T13:58:43.201+0000 I REPL     [rsSync] transition to SECONDARY
2018-09-24T13:58:59.208+0000 I REPL     [rsBackgroundSync] sync source candidate: 192.168.103.100:25001

  • Once the sync source node is selected, SyncSourceResolver probes the sync source to confirm that it is able to fetch the oplogs.
  • RollbackID is also fetched i.e. rbid  after the first batch is returned by oplogfetcher.
  • If all eligible sync sources are too fresh, such as during initial sync, then the syncSourceStatus Oplog start is missing and earliestOpTimeSeen will set a new minValid.
  • This minValid is also set in the case of rollback and abrupt shutdown.
  • If the node has a minValid entry then this is checked for the eligible sync source node.

Example showing the selection of a new sync source when the existing source is found to be invalid

Here, as the logs show, during sync the node chooses a new sync source. This is because it found the original sync source is not ahead, so not does not contain recent oplogs from which to sync.

2018-09-25T15:20:55.424+0000 I REPL     [replication-1] Choosing new sync source because our current sync source, 192.168.103.100:25001, has an OpTime ({ ts: Timestamp 1537879296000|1, t: 4 }) which is not ahead of ours ({ ts: Timestamp 1537879296000|1, t: 4 }), it does not have a sync source, and it's not the primary (sync source does not know the primary)

2018-09-25T15:20:55.425+0000 W REPL [rsBackgroundSync] Fetcher stopped querying remote oplog with error: InvalidSyncSource: sync source 192.168.103.100:25001 (config version: 3; last applied optime: { ts: Timestamp 1537879296000|1, t: 4 }; sync source index: -1; primary index: -1) is no longer valid

  • If the secondary node is too far behind the eligible sync source node, then the node will enter maintenance node and then resync needs to be call manually.
  • Once the sync source is chosen, BackgroundSync starts oplogFetcher.

Example for oplogFetcher

Here is an example of fetching oplog from the “oplog.rs” collection, and checking for the greater than required timestamp.

2018-09-26T10:35:07.372+0000 I COMMAND  [conn113] command local.oplog.rs command: getMore { getMore: 20830044306, collection: "oplog.rs", maxTimeMS: 5000, term: 7, lastKnownCommittedOpTime: { ts: Timestamp 1537955038000|1, t: 7 } } originatingCommand: { find: "oplog.rs", filter: { ts: { $gte: Timestamp 1537903865000|1 } }, tailable: true, oplogReplay: true, awaitData: true, maxTimeMS: 60000, term: 7, readConcern: { afterOpTime: { ts: Timestamp 1537903865000|1, t: 6 } } } planSummary: COLLSCAN cursorid:20830044306 keysExamined:0 docsExamined:0 numYields:1 nreturned:0 reslen:451 locks:{ Global: { acquireCount: { r: 6 } }, Database: { acquireCount: { r: 3 } }, oplog: { acquireCount: { r: 3 } } } protocol:op_command 3063398ms

When and what details replica set nodes communicate with each other?

At a regular interval, all the nodes communicate with each other to check the status of the primary node, check the status of the sync source, to get the oplogs and so on.

ReplicationCoordinator has ReplicaSetConfig that has a list of all the replica set nodes, and each node has a copy of it. This makes nodes aware of other nodes under same replica set.

This is how nodes communicate in more detail:

Heartbeats: This checks the status of other nodes i.e. alive or die

heartbeatInterval: Every node, at an interval of two seconds, sends the other nodes a heartbeat to make them aware that “yes I am alive!”

heartbeatTimeoutSecs: This is a timeout, and means that if the heartbeat is not returned in 10 seconds then that node is marked as inaccessible or simply die.

Every heartbeat is identified by these replica set details:

  • replica set config version
  • replica set name
  • Sender host address
  • id from the replicasetconfig

The source code could be referred to from here.

When the remote node receives the heartbeat, it processes this data and validates if the details are correct. It then prepares a ReplSetHeartbeatResponse, that includes:

  • Name of the replica set, config version, and optime details
  • Details about primary node as per the receiving node.
  • Sync source details and state of receiving node

This heartbeat data is processed, and if primary details are found then the election gets postponed.

TopologyCoordinator checks for the heartbeat data and confirms if the node is OK or NOT. If the node is OK then no action is taken. Otherwise it needs to be reconfigured or else initiate a priority takeover based on the config.

Response from oplog fetcher

To get the oplogs from the sync source, nodes communicate with each other. This oplog fetcher fetches oplogs through “find” and “getMore”. This will only affect the downstream node that gets metadata from its sync source to update its view from the replica set.

OplogQueryMetadata only comes with OplogFetcher responses

OplogQueryMetadata comes with OplogFetcher response and ReplSetMetadata comes with all the replica set details including configversion and replication commands.

Communicate to update Position commands:

This is to get an update for replication progress. ReplicationCoordinatorExternalState creates SyncSourceFeedback sends replSetUpdatePosition commands.

It includes Oplog details, Replicaset config version, and replica set metadata.

If a new node is added to the existing replica set, how will that node get the data?

If a new node is added to the existing replica set then the “initial sync” process takes place. This initial sync can be done in two ways:

  1. Just add the new node to the replicaset and let initial sync threads restore the data. Then it syncs from the oplogs until it reaches the secondary state.
  2. Copy the data from the recent data directory to the node, and restart this new node. Then it will also sync from the oplogs until it reaches the secondary state.

This is how it works internally

When “initial sync” or “rsync” is called by ReplicationCoordinator  then the node goes to “STARTUP2” state, and this initial sync is done in DataReplicator

  • A sync source is selected to get the data from, then it drops all the databases except the local database, and oplogs are recreated.
  • DatabasesCloner asks syncsource for a list of the databases, and for each database it creates DatabaseCloner.
  • For each DatabaseCloner it creates CollectionCloner to clone the collections
  • This CollectionCloner calls ListIndexes on the syncsource and creates a CollectionBulkLoader for parallel index creation while data cloning
  • The node also checks for the sync source rollback id. If rollback occurred, then it restarts the initial sync. Otherwise, datareplicator is done with its work and then replicationCoordinator assumes the role for ongoing replication.

Example for the “initial sync” :

Here node enters  

"STARTUP2"- "transition to STARTUP2"

Then sync source gets selected and drops all the databases except the local database.  Next, replication oplog is created and CollectionCloner is called.

Local database not dropped: because every node has its own “local” database with its own and other nodes’ information, based on itself, this database is not replicated to other nodes.

2018-09-26T17:57:09.571+0000 I REPL     [ReplicationExecutor] transition to STARTUP2
2018-09-26T17:57:14.589+0000 I REPL     [replication-1] sync source candidate: 192.168.103.100:25003
2018-09-26T17:57:14.590+0000 I STORAGE  [replication-1] dropAllDatabasesExceptLocal 1
2018-09-26T17:57:14.592+0000 I REPL     [replication-1] creating replication oplog of size: 990MB... 2018-09-26T17:57:14.633+0000 I REPL     [replication-0] CollectionCloner::start called, on ns:admin.system.version

Finished fetching all the oplogs, and finishing up initial sync.

2018-09-26T17:57:15.685+0000 I REPL     [replication-0] Finished fetching oplog during initial sync: CallbackCanceled: Callback canceled. Last fetched optime and hash: { ts: Timestamp 1537984626000|1, t: 9 }[-1139925876765058240]
2018-09-26T17:57:15.685+0000 I REPL     [replication-0] Initial sync attempt finishing up.

What are oplogs and where do these reside?

oplogs stands for “operation logs”. We have used this term so many times in this blog post as these are the mandatory logs for the replica set. These operations are in the capped collection called “oplog.rs”  that resides in “local” database.

Below, this is how oplogs are stored in the collection “oplog.rs” that includes details for timestamp, operations, namespace, output.

rplint:PRIMARY> use local
rplint:PRIMARY> show collections
oplog.rs
rplint:PRIMARY> db.oplog.rs.findOne()
{
 "ts" : Timestamp(1537797392, 1),
 "h" : NumberLong("-169301588285533642"),
 "v" : 2,
 "op" : "n",
 "ns" : "",
 "o" : {
 "msg" : "initiating set"
 }
}

It consists of rolling update operations coming to the database. Then these oplogs replicate to the secondary node(s) to maintain the high availability of the data in case of failover.

When the replica MongoDB instance starts, it creates an oplog ocdefault size. For Wired tiger, the default size is 5% of disk space, with a lower bound size of 990MB. So here in the example it creates 990MB of data. If you’d like to learn more about oplog size then please refer here

2018-09-26T17:57:14.592+0000 I REPL     [replication-1] creating replication oplog of size: 990MB...

What if the same oplog is applied multiple times, will that not lead to inconsistent data?

Fortunately, oplogs are Idempotent that means the value will remain unchanged, or will provide the same output, even when applied multiple times.

Let’s check an example:

For the $inc operator that will increment the value by 1 for the filed “item”, if this oplog is applied multiple times then the result might lead to an inconsistent record if this is not Idempotent. However, rather than increasing the item value multiple times, it is actually applied only once.

rplint:PRIMARY> use db1
//inserting one document
rplint:PRIMARY> db.col1.insert({item:1, name:"abc"})
//updating document by incrementing item value with 1
rplint:PRIMARY> db.col1.update({name:"abc"},{$inc:{item:1}})
//updated value is now item:2
rplint:PRIMARY> db.col1.find()
{ "_id" : ObjectId("5babd57cce2ef78096ac8e16"), "item" : 2, "name" : "abc" }

This is how these operations are stored in oplog, here this $inc value is stored in oplog as $set

rplint:PRIMARY> db.oplog.rs.find({ns:"db1.col1"})
//insert operation
{ "ts" : Timestamp(1537987964, 2), "t" : NumberLong(9), "h" : NumberLong("8083740413874479202"), "v" : 2, "op" : "i", "ns" : "db1.col1", "o" : { "_id" : ObjectId("5babd57cce2ef78096ac8e16"), "item" : 1, "name" : "abc" } }
//$inc operation is changed as ""$set" : { "item" : 2"
{ "ts" : Timestamp(1537988022, 1), "t" : NumberLong(9), "h" : NumberLong("-1432987813358665721"), "v" : 2, "op" : "u", "ns" : "db1.col1", "o2" : { "_id" : ObjectId("5babd57cce2ef78096ac8e16") }, "o" : { "$set" : { "item" : 2 } } }

That means that however many  times it is applied, it will generate the same results, so no inconsistent data!

I hope this blog post helps you to understand multiple scenarios for MongoDB replica sets, and how data replicates to the nodes.

Jun
13
2018
--

Webinar Thurs 6/14: MongoDB Backup and Recovery Field Guide

mongodb backup and recovery field guide

mongodb backup and recovery field guidePlease join Percona’s Sr. Technical Operations Architect, Tim Vaillancourt as he presents MongoDB Backup and Recovery Field Guide on Thursday, June 14, 2018, at 10:00 AM PDT (UTC-7) / 1:00 PM EDT (UTC-4).

This talk will cover backup and recovery solutions for MongoDB replica sets and clusters, focusing on online and low-impact solutions for production systems.

Register for the webinar

Tim Vaillancourt

Senior Technical Operations Architect

With experience operating infrastructures in industries such as government, online marketing/publishing, SaaS and gaming combined with experience tuning systems from the hard disk all the way up to the end-user, Tim has spent time in nearly every area of the modern IT stack with many lessons learned.

Tim is based in Amsterdam, NL and enjoys traveling, coding and music. Prior to Percona Tim was the Lead MySQL DBA of Electronic Arts’ DICE studios, helping some of the largest games in the world (“Battlefield” series, “Mirrors Edge” series, “Star Wars: Battlefront”) launch and operate smoothly while also leading the automation of MongoDB deployments for EA systems. Before the role of DBA at EA’s DICE studio, Tim served as a subject matter expert in NoSQL databases, queues and search on the Online Operations team at EA SPORTS.

Prior to moving to the gaming industry, Tim served as a Database/Systems Admin operating a large MySQL-based SaaS infrastructure at AbeBooks/Amazon Inc.

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