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View the memory and concurrency limits allocated to the various performance levels and resource classes in Azure Synapse Analytics.

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Data warehouse capacity settings

The following tables show the maximum capacity for the data warehouse at different performance levels. To change the performance level, see Scale compute - portal.

Service Levels

The service levels range from DW100c to DW30000c.

Performance level	Compute nodes	Distributions per Compute node	Memory per data warehouse (GB)
DW100c	1	60	60
DW200c	1	60	120
DW300c	1	60	180
DW400c	1	60	240
DW500c	1	60	300
DW1000c	2	30	600
DW1500c	3	20	900
DW2000c	4	15	1200
DW2500c	5	12	1500
DW3000c	6	10	1800
DW5000c	10	6	3000
DW6000c	12	5	3600
DW7500c	15	4	4500
DW10000c	20	3	6000
DW15000c	30	2	9000
DW30000c	60	1	18000

The maximum service level is DW30000c, which has 60 Compute nodes and one distribution per Compute node. For example, a 600 TB data warehouse at DW30000c processes approximately 10 TB per Compute node.

Concurrency maximums for workload groups

With the introduction of workload groups, the concept of concurrency slots no longer applies. Resources per request are allocated on a percentage basis and specified in the workload group definition. However, even with the removal of concurrency slots, there are minimum amounts of resources needed per queries based on the service level. The below table defined the minimum amount of resources needed per query across service levels and the associated concurrency that can be achieved.

Service Level	Maximum concurrent queries	Min % supported for REQUEST_MIN_RESOURCE_GRANT_PERCENT
DW100c	4	25%
DW200c	8	12.5%
DW300c	12	8%
DW400c	16	6.25%
DW500c	20	5%
DW1000c	32	3%
DW1500c	32	3%
DW2000c	48	2%
DW2500c	48	2%
DW3000c	64	1.5%
DW5000c	64	1.5%
DW6000c	128	0.75%
DW7500c	128	0.75%
DW10000c	128	0.75%
DW15000c	128	0.75%
DW30000c	128	0.75%

Concurrency maximums for resource classes

To ensure each query has enough resources to execute efficiently, Synapse SQL tracks resource utilization by assigning concurrency slots to each query. The system puts queries into a queue based on importance and concurrency slots. Queries wait in the queue until enough concurrency slots are available. Importance and concurrency slots determine CPU prioritization. For more information, see Analyze your workload

Static resource classes

The following table shows the maximum concurrent queries and concurrency slots for each static resource class.

Service Level	Maximum concurrent queries	Concurrency slots available	Slots used by staticrc10	Slots used by staticrc20	Slots used by staticrc30	Slots used by staticrc40	Slots used by staticrc50	Slots used by staticrc60	Slots used by staticrc70	Slots used by staticrc80
DW100c	4	4	1	2	4	4	4	4	4	4
DW200c	8	8	1	2	4	8	8	8	8	8
DW300c	12	12	1	2	4	8	8	8	8	8
DW400c	16	16	1	2	4	8	16	16	16	16
DW500c	20	20	1	2	4	8	16	16	16	16
DW1000c	32	40	1	2	4	8	16	32	32	32
DW1500c	32	60	1	2	4	8	16	32	32	32
DW2000c	48	80	1	2	4	8	16	32	64	64
DW2500c	48	100	1	2	4	8	16	32	64	64
DW3000c	64	120	1	2	4	8	16	32	64	64
DW5000c	64	200	1	2	4	8	16	32	64	128
DW6000c	128	240	1	2	4	8	16	32	64	128
DW7500c	128	300	1	2	4	8	16	32	64	128
DW10000c	128	400	1	2	4	8	16	32	64	128
DW15000c	128	600	1	2	4	8	16	32	64	128
DW30000c	128	1200	1	2	4	8	16	32	64	128

Dynamic resource classes

The following table shows the maximum concurrent queries and concurrency slots for each dynamic resource class. Dynamic resource classes use a 3-10-22-70 memory percentage allocation for small-medium-large-xlarge resource classes across all service levels.

Service Level	Maximum concurrent queries	Concurrency slots available	Slots used by smallrc	Slots used by mediumrc	Slots used by largerc	Slots used by xlargerc
DW100c	4	4	1	1	1	2
DW200c	8	8	1	1	1	5
DW300c	12	12	1	1	2	8
DW400c	16	16	1	1	3	11
DW500c	20	20	1	2	4	14
DW1000c	32	40	1	4	8	28
DW1500c	32	60	1	6	13	42
DW2000c	32	80	2	8	17	56
DW2500c	32	100	3	10	22	70
DW3000c	32	120	3	12	26	84
DW5000c	32	200	6	20	44	140
DW6000c	32	240	7	24	52	168
DW7500c	32	300	9	30	66	210
DW10000c	32	400	12	40	88	280
DW15000c	32	600	18	60	132	420
DW30000c	32	1200	36	120	264	840

When there are not enough concurrency slots free to start query execution, queries are queued and executed based on importance. If there is equivalent importance, queries are executed on a first-in, first-out basis. As a queries finishes and the number of queries and slots fall below the limits, Azure Synapse Analytics releases queued queries.

Next steps

To learn more about how to leverage resource classes to optimize your workload further please review the following articles:

Bypassing Player Slot Limits

Most small servers nowadays are limited by the server's player slot limit.
The following example will show one of many concepts of bypassing the server player slot limit.
First, let us create our Free

main and only class for this example:

packageme.web.playerlimitbypass;
importorg.bukkit.event.Listener;
importorg.bukkit.plugin.java.JavaPlugin;
publicclass PlayerBypass extends JavaPlugin implements Listener {
@Override
publicvoid onEnable(){
}
}

Pretty simple.
Next, we will create our Event Handler and register the event:

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@EventHandler
publicvoid onPlayerLoginEvent(PlayerLoginEvent event){
}

@Override
publicvoid onEnable(){
// Registering the events
getServer().getPluginManager().registerEvents(this, this);
}

The following code is the very base of this concept:

@EventHandler
publicvoid onPlayerLoginEvent(PlayerLoginEvent event){
// Checking if the reason we are being kicked is a full server
if(event.getResult() PlayerLoginEvent.Result.KICK_FULL){
// If the condition above is true, we execute the following code, that is allow the player on the server
event.allow();
}
}

You can tweak the base any way you want. For instance, check if the player has permissions to bypass the player limit:

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@EventHandler
publicvoid onPlayerLoginEvent(PlayerLoginEvent event){
// Checking if the reason we are being kicked is a full server
if(event.getResult() PlayerLoginEvent.Result.KICK_FULL){
// Checking if the player has the specified permission node
if(event.getPlayer().hasPermission('playerlimit.bypass'))
// If the condition above is true, we execute the following code, that allows the player on the server
event.allow();
}
}