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  • Cisco ASA: Allowing and Denying VPN Access based on membership to an AD group

    - by milkandtang
    I have a Cisco ASA 5505 connecting to an Active Directory server for VPN authentication. Usually we'd restrict this to a particular OU, but in this case users which need access are spread across multiple OUs. So, I'd like to use a group to specify which users have remote access. I've created the group and added the users, but I'm having trouble figuring out how to deny users which aren't in that group. Right now, if someone connects they get assigned the correct group policy "companynamera" if they are in that group, so the LDAP mapping is working. However, users who are not in that group still authenticate fine, and their group policy becomes the LDAP path of their first group, i.e. CN=Domain Users,CN=Users,DC=example,DC=com, and then are still allowed access. How do I add a filter so that I can map everything that isn't "companynamera" to no access? Config I'm using (with some stuff such as ACLs and mappings removed, since they are just noise here): gateway# show run : Saved : ASA Version 8.2(1) ! hostname gateway domain-name corp.company-name.com enable password gDZcqZ.aUC9ML0jK encrypted passwd gDZcqZ.aUC9ML0jK encrypted names name 192.168.0.2 dc5 description FTP Server name 192.168.0.5 dc2 description Everything server name 192.168.0.6 dc4 description File Server name 192.168.0.7 ts1 description Light Use Terminal Server name 192.168.0.8 ts2 description Heavy Use Terminal Server name 4.4.4.82 primary-frontier name 5.5.5.26 primary-eschelon name 172.21.18.5 dmz1 description Kerio Mail Server and FTP Server name 4.4.4.84 ts-frontier name 4.4.4.85 vpn-frontier name 5.5.5.28 ts-eschelon name 5.5.5.29 vpn-eschelon name 5.5.5.27 email-eschelon name 4.4.4.83 guest-frontier name 4.4.4.86 email-frontier dns-guard ! interface Vlan1 nameif inside security-level 100 ip address 192.168.0.254 255.255.255.0 ! interface Vlan2 description Frontier FiOS nameif outside security-level 0 ip address primary-frontier 255.255.255.0 ! interface Vlan3 description Eschelon T1 nameif backup security-level 0 ip address primary-eschelon 255.255.255.248 ! interface Vlan4 nameif dmz security-level 50 ip address 172.21.18.254 255.255.255.0 ! interface Vlan5 nameif guest security-level 25 ip address 172.21.19.254 255.255.255.0 ! interface Ethernet0/0 switchport access vlan 2 ! interface Ethernet0/1 switchport access vlan 3 ! interface Ethernet0/2 switchport access vlan 4 ! interface Ethernet0/3 switchport access vlan 5 ! interface Ethernet0/4 ! interface Ethernet0/5 ! interface Ethernet0/6 ! interface Ethernet0/7 ! ftp mode passive clock timezone PST -8 clock summer-time PDT recurring dns domain-lookup inside dns server-group DefaultDNS name-server dc2 domain-name corp.company-name.com same-security-traffic permit intra-interface access-list companyname_splitTunnelAcl standard permit 192.168.0.0 255.255.255.0 access-list companyname_splitTunnelAcl standard permit 172.21.18.0 255.255.255.0 access-list inside_nat0_outbound extended permit ip any 172.21.20.0 255.255.255.0 access-list inside_nat0_outbound extended permit ip any 172.21.18.0 255.255.255.0 access-list bypassingnat_dmz extended permit ip 172.21.18.0 255.255.255.0 192.168.0.0 255.255.255.0 pager lines 24 logging enable logging buffer-size 12288 logging buffered warnings logging asdm notifications mtu inside 1500 mtu outside 1500 mtu backup 1500 mtu dmz 1500 mtu guest 1500 ip local pool VPNpool 172.21.20.50-172.21.20.59 mask 255.255.255.0 no failover icmp unreachable rate-limit 1 burst-size 1 no asdm history enable arp timeout 14400 global (outside) 1 interface global (outside) 2 email-frontier global (outside) 3 guest-frontier global (backup) 1 interface global (dmz) 1 interface nat (inside) 0 access-list inside_nat0_outbound nat (inside) 2 dc5 255.255.255.255 nat (inside) 1 192.168.0.0 255.255.255.0 nat (dmz) 0 access-list bypassingnat_dmz nat (dmz) 2 dmz1 255.255.255.255 nat (dmz) 1 172.21.18.0 255.255.255.0 access-group outside_access_in in interface outside access-group dmz_access_in in interface dmz route outside 0.0.0.0 0.0.0.0 4.4.4.1 1 track 1 route backup 0.0.0.0 0.0.0.0 5.5.5.25 254 timeout xlate 3:00:00 timeout conn 1:00:00 half-closed 0:10:00 udp 0:02:00 icmp 0:00:02 timeout sunrpc 0:10:00 h323 0:05:00 h225 1:00:00 mgcp 0:05:00 mgcp-pat 0:05:00 timeout sip 0:30:00 sip_media 0:02:00 sip-invite 0:03:00 sip-disconnect 0:02:00 timeout sip-provisional-media 0:02:00 uauth 0:05:00 absolute timeout tcp-proxy-reassembly 0:01:00 ldap attribute-map RemoteAccessMap map-name memberOf IETF-Radius-Class map-value memberOf CN=RemoteAccess,CN=Users,DC=corp,DC=company-name,DC=com companynamera dynamic-access-policy-record DfltAccessPolicy aaa-server ActiveDirectory protocol ldap aaa-server ActiveDirectory (inside) host dc2 ldap-base-dn dc=corp,dc=company-name,dc=com ldap-scope subtree ldap-login-password * ldap-login-dn cn=administrator,ou=Admins,dc=corp,dc=company-name,dc=com server-type microsoft aaa-server ADRemoteAccess protocol ldap aaa-server ADRemoteAccess (inside) host dc2 ldap-base-dn dc=corp,dc=company-name,dc=com ldap-scope subtree ldap-login-password * ldap-login-dn cn=administrator,ou=Admins,dc=corp,dc=company-name,dc=com server-type microsoft ldap-attribute-map RemoteAccessMap aaa authentication enable console LOCAL aaa authentication ssh console LOCAL http server enable http 192.168.0.0 255.255.255.0 inside no snmp-server location no snmp-server contact snmp-server enable traps snmp authentication linkup linkdown coldstart sla monitor 123 type echo protocol ipIcmpEcho 4.4.4.1 interface outside num-packets 3 frequency 10 sla monitor schedule 123 life forever start-time now crypto ipsec transform-set ESP-3DES-SHA esp-3des esp-sha-hmac crypto ipsec security-association lifetime seconds 28800 crypto ipsec security-association lifetime kilobytes 4608000 crypto dynamic-map outside_dyn_map 20 set pfs crypto dynamic-map outside_dyn_map 20 set transform-set ESP-3DES-SHA crypto map outside_map 65535 ipsec-isakmp dynamic outside_dyn_map crypto map outside_map interface outside crypto isakmp enable outside crypto isakmp policy 10 authentication pre-share encryption 3des hash sha group 2 lifetime 86400 ! track 1 rtr 123 reachability telnet timeout 5 ssh 192.168.0.0 255.255.255.0 inside ssh timeout 5 ssh version 2 console timeout 0 management-access inside dhcpd auto_config outside ! threat-detection basic-threat threat-detection statistics access-list no threat-detection statistics tcp-intercept webvpn group-policy companynamera internal group-policy companynamera attributes wins-server value 192.168.0.5 dns-server value 192.168.0.5 vpn-tunnel-protocol IPSec password-storage enable split-tunnel-policy tunnelspecified split-tunnel-network-list value companyname_splitTunnelAcl default-domain value corp.company-name.com split-dns value corp.company-name.com group-policy companyname internal group-policy companyname attributes wins-server value 192.168.0.5 dns-server value 192.168.0.5 vpn-tunnel-protocol IPSec password-storage enable split-tunnel-policy tunnelspecified split-tunnel-network-list value companyname_splitTunnelAcl default-domain value corp.company-name.com split-dns value corp.company-name.com username admin password IhpSqtN210ZsNaH. encrypted privilege 15 tunnel-group companyname type remote-access tunnel-group companyname general-attributes address-pool VPNpool authentication-server-group ActiveDirectory LOCAL default-group-policy companyname tunnel-group companyname ipsec-attributes pre-shared-key * tunnel-group companynamera type remote-access tunnel-group companynamera general-attributes address-pool VPNpool authentication-server-group ADRemoteAccess LOCAL default-group-policy companynamera tunnel-group companynamera ipsec-attributes pre-shared-key * ! class-map type inspect ftp match-all ftp-inspection-map class-map inspection_default match default-inspection-traffic ! ! policy-map type inspect ftp ftp-inspection-map parameters class ftp-inspection-map policy-map type inspect dns migrated_dns_map_1 parameters message-length maximum 512 policy-map global_policy class inspection_default inspect dns migrated_dns_map_1 inspect ftp inspect h323 h225 inspect h323 ras inspect http inspect ils inspect netbios inspect rsh inspect rtsp inspect skinny inspect sqlnet inspect sunrpc inspect tftp inspect sip inspect xdmcp inspect icmp inspect icmp error inspect esmtp inspect pptp ! service-policy global_policy global prompt hostname context Cryptochecksum:487525494a81c8176046fec475d17efe : end gateway# Thanks so much!

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • The broken Promise of the Mobile Web

    - by Rick Strahl
    High end mobile devices have been with us now for almost 7 years and they have utterly transformed the way we access information. Mobile phones and smartphones that have access to the Internet and host smart applications are in the hands of a large percentage of the population of the world. In many places even very remote, cell phones and even smart phones are a common sight. I’ll never forget when I was in India in 2011 I was up in the Southern Indian mountains riding an elephant out of a tiny local village, with an elephant herder in front riding atop of the elephant in front of us. He was dressed in traditional garb with the loin wrap and head cloth/turban as did quite a few of the locals in this small out of the way and not so touristy village. So we’re slowly trundling along in the forest and he’s lazily using his stick to guide the elephant and… 10 minutes in he pulls out his cell phone from his sash and starts texting. In the middle of texting a huge pig jumps out from the side of the trail and he takes a picture running across our path in the jungle! So yeah, mobile technology is very pervasive and it’s reached into even very buried and unexpected parts of this world. Apps are still King Apps currently rule the roost when it comes to mobile devices and the applications that run on them. If there’s something that you need on your mobile device your first step usually is to look for an app, not use your browser. But native app development remains a pain in the butt, with the requirement to have to support 2 or 3 completely separate platforms. There are solutions that try to bridge that gap. Xamarin is on a tear at the moment, providing their cross-device toolkit to build applications using C#. While Xamarin tools are impressive – and also *very* expensive – they only address part of the development madness that is app development. There are still specific device integration isssues, dealing with the different developer programs, security and certificate setups and all that other noise that surrounds app development. There’s also PhoneGap/Cordova which provides a hybrid solution that involves creating local HTML/CSS/JavaScript based applications, and then packaging them to run in a specialized App container that can run on most mobile device platforms using a WebView interface. This allows for using of HTML technology, but it also still requires all the set up, configuration of APIs, security keys and certification and submission and deployment process just like native applications – you actually lose many of the benefits that  Web based apps bring. The big selling point of Cordova is that you get to use HTML have the ability to build your UI once for all platforms and run across all of them – but the rest of the app process remains in place. Apps can be a big pain to create and manage especially when we are talking about specialized or vertical business applications that aren’t geared at the mainstream market and that don’t fit the ‘store’ model. If you’re building a small intra department application you don’t want to deal with multiple device platforms and certification etc. for various public or corporate app stores. That model is simply not a good fit both from the development and deployment perspective. Even for commercial, big ticket apps, HTML as a UI platform offers many advantages over native, from write-once run-anywhere, to remote maintenance, single point of management and failure to having full control over the application as opposed to have the app store overloads censor you. In a lot of ways Web based HTML/CSS/JavaScript applications have so much potential for building better solutions based on existing Web technologies for the very same reasons a lot of content years ago moved off the desktop to the Web. To me the Web as a mobile platform makes perfect sense, but the reality of today’s Mobile Web unfortunately looks a little different… Where’s the Love for the Mobile Web? Yet here we are in the middle of 2014, nearly 7 years after the first iPhone was released and brought the promise of rich interactive information at your fingertips, and yet we still don’t really have a solid mobile Web platform. I know what you’re thinking: “But we have lots of HTML/JavaScript/CSS features that allows us to build nice mobile interfaces”. I agree to a point – it’s actually quite possible to build nice looking, rich and capable Web UI today. We have media queries to deal with varied display sizes, CSS transforms for smooth animations and transitions, tons of CSS improvements in CSS 3 that facilitate rich layout, a host of APIs geared towards mobile device features and lately even a number of JavaScript framework choices that facilitate development of multi-screen apps in a consistent manner. Personally I’ve been working a lot with AngularJs and heavily modified Bootstrap themes to build mobile first UIs and that’s been working very well to provide highly usable and attractive UI for typical mobile business applications. From the pure UI perspective things actually look very good. Not just about the UI But it’s not just about the UI - it’s also about integration with the mobile device. When it comes to putting all those pieces together into what amounts to a consolidated platform to build mobile Web applications, I think we still have a ways to go… there are a lot of missing pieces to make it all work together and integrate with the device more smoothly, and more importantly to make it work uniformly across the majority of devices. I think there are a number of reasons for this. Slow Standards Adoption HTML standards implementations and ratification has been dreadfully slow, and browser vendors all seem to pick and choose different pieces of the technology they implement. The end result is that we have a capable UI platform that’s missing some of the infrastructure pieces to make it whole on mobile devices. There’s lots of potential but what is lacking that final 10% to build truly compelling mobile applications that can compete favorably with native applications. Some of it is the fragmentation of browsers and the slow evolution of the mobile specific HTML APIs. A host of mobile standards exist but many of the standards are in the early review stage and they have been there stuck for long periods of time and seem to move at a glacial pace. Browser vendors seem even slower to implement them, and for good reason – non-ratified standards mean that implementations may change and vendor implementations tend to be experimental and  likely have to be changed later. Neither Vendors or developers are not keen on changing standards. This is the typical chicken and egg scenario, but without some forward momentum from some party we end up stuck in the mud. It seems that either the standards bodies or the vendors need to carry the torch forward and that doesn’t seem to be happening quickly enough. Mobile Device Integration just isn’t good enough Current standards are not far reaching enough to address a number of the use case scenarios necessary for many mobile applications. While not every application needs to have access to all mobile device features, almost every mobile application could benefit from some integration with other parts of the mobile device platform. Integration with GPS, phone, media, messaging, notifications, linking and contacts system are benefits that are unique to mobile applications and could be widely used, but are mostly (with the exception of GPS) inaccessible for Web based applications today. Unfortunately trying to do most of this today only with a mobile Web browser is a losing battle. Aside from PhoneGap/Cordova’s app centric model with its own custom API accessing mobile device features and the token exception of the GeoLocation API, most device integration features are not widely supported by the current crop of mobile browsers. For example there’s no usable messaging API that allows access to SMS or contacts from HTML. Even obvious components like the Media Capture API are only implemented partially by mobile devices. There are alternatives and workarounds for some of these interfaces by using browser specific code, but that’s might ugly and something that I thought we were trying to leave behind with newer browser standards. But it’s not quite working out that way. It’s utterly perplexing to me that mobile standards like Media Capture and Streams, Media Gallery Access, Responsive Images, Messaging API, Contacts Manager API have only minimal or no traction at all today. Keep in mind we’ve had mobile browsers for nearly 7 years now, and yet we still have to think about how to get access to an image from the image gallery or the camera on some devices? Heck Windows Phone IE Mobile just gained the ability to upload images recently in the Windows 8.1 Update – that’s feature that HTML has had for 20 years! These are simple concepts and common problems that should have been solved a long time ago. It’s extremely frustrating to see build 90% of a mobile Web app with relative ease and then hit a brick wall for the remaining 10%, which often can be show stoppers. The remaining 10% have to do with platform integration, browser differences and working around the limitations that browsers and ‘pinned’ applications impose on HTML applications. The maddening part is that these limitations seem arbitrary as they could easily work on all mobile platforms. For example, SMS has a URL Moniker interface that sort of works on Android, works badly with iOS (only works if the address is already in the contact list) and not at all on Windows Phone. There’s no reason this shouldn’t work universally using the same interface – after all all phones have supported SMS since before the year 2000! But, it doesn’t have to be this way Change can happen very quickly. Take the GeoLocation API for example. Geolocation has taken off at the very beginning of the mobile device era and today it works well, provides the necessary security (a big concern for many mobile APIs), and is supported by just about all major mobile and even desktop browsers today. It handles security concerns via prompts to avoid unwanted access which is a model that would work for most other device APIs in a similar fashion. One time approval and occasional re-approval if code changes or caches expire. Simple and only slightly intrusive. It all works well, even though GeoLocation actually has some physical limitations, such as representing the current location when no GPS device is present. Yet this is a solved problem, where other APIs that are conceptually much simpler to implement have failed to gain any traction at all. Technically none of these APIs should be a problem to implement, but it appears that the momentum is just not there. Inadequate Web Application Linking and Activation Another important piece of the puzzle missing is the integration of HTML based Web applications. Today HTML based applications are not first class citizens on mobile operating systems. When talking about HTML based content there’s a big difference between content and applications. Content is great for search engine discovery and plain browser usage. Content is usually accessed intermittently and permanent linking is not so critical for this type of content.  But applications have different needs. Applications need to be started up quickly and must be easily switchable to support a multi-tasking user workflow. Therefore, it’s pretty crucial that mobile Web apps are integrated into the underlying mobile OS and work with the standard task management features. Unfortunately this integration is not as smooth as it should be. It starts with actually trying to find mobile Web applications, to ‘installing’ them onto a phone in an easily accessible manner in a prominent position. The experience of discovering a Mobile Web ‘App’ and making it sticky is by no means as easy or satisfying. Today the way you’d go about this is: Open the browser Search for a Web Site in the browser with your search engine of choice Hope that you find the right site Hope that you actually find a site that works for your mobile device Click on the link and run the app in a fully chrome’d browser instance (read tiny surface area) Pin the app to the home screen (with all the limitations outline above) Hope you pointed at the right URL when you pinned Even for you and me as developers, there are a few steps in there that are painful and annoying, but think about the average user. First figuring out how to search for a specific site or URL? And then pinning the app and hopefully from the right location? You’ve probably lost more than half of your audience at that point. This experience sucks. For developers too this process is painful since app developers can’t control the shortcut creation directly. This problem often gets solved by crazy coding schemes, with annoying pop-ups that try to get people to create shortcuts via fancy animations that are both annoying and add overhead to each and every application that implements this sort of thing differently. And that’s not the end of it - getting the link onto the home screen with an application icon varies quite a bit between browsers. Apple’s non-standard meta tags are prominent and they work with iOS and Android (only more recent versions), but not on Windows Phone. Windows Phone instead requires you to create an actual screen or rather a partial screen be captured for a shortcut in the tile manager. Who had that brilliant idea I wonder? Surprisingly Chrome on recent Android versions seems to actually get it right – icons use pngs, pinning is easy and pinned applications properly behave like standalone apps and retain the browser’s active page state and content. Each of the platforms has a different way to specify icons (WP doesn’t allow you to use an icon image at all), and the most widely used interface in use today is a bunch of Apple specific meta tags that other browsers choose to support. The question is: Why is there no standard implementation for installing shortcuts across mobile platforms using an official format rather than a proprietary one? Then there’s iOS and the crazy way it treats home screen linked URLs using a crazy hybrid format that is neither as capable as a Web app running in Safari nor a WebView hosted application. Moving off the Web ‘app’ link when switching to another app actually causes the browser and preview it to ‘blank out’ the Web application in the Task View (see screenshot on the right). Then, when the ‘app’ is reactivated it ends up completely restarting the browser with the original link. This is crazy behavior that you can’t easily work around. In some situations you might be able to store the application state and restore it using LocalStorage, but for many scenarios that involve complex data sources (like say Google Maps) that’s not a possibility. The only reason for this screwed up behavior I can think of is that it is deliberate to make Web apps a pain in the butt to use and forcing users trough the App Store/PhoneGap/Cordova route. App linking and management is a very basic problem – something that we essentially have solved in every desktop browser – yet on mobile devices where it arguably matters a lot more to have easy access to web content we have to jump through hoops to have even a remotely decent linking/activation experience across browsers. Where’s the Money? It’s not surprising that device home screen integration and Mobile Web support in general is in such dismal shape – the mobile OS vendors benefit financially from App store sales and have little to gain from Web based applications that bypass the App store and the cash cow that it presents. On top of that, platform specific vendor lock-in of both end users and developers who have invested in hardware, apps and consumables is something that mobile platform vendors actually aspire to. Web based interfaces that are cross-platform are the anti-thesis of that and so again it’s no surprise that the mobile Web is on a struggling path. But – that may be changing. More and more we’re seeing operations shifting to services that are subscription based or otherwise collect money for usage, and that may drive more progress into the Web direction in the end . Nothing like the almighty dollar to drive innovation forward. Do we need a Mobile Web App Store? As much as I dislike moderated experiences in today’s massive App Stores, they do at least provide one single place to look for apps for your device. I think we could really use some sort of registry, that could provide something akin to an app store for mobile Web apps, to make it easier to actually find mobile applications. This could take the form of a specialized search engine, or maybe a more formal store/registry like structure. Something like apt-get/chocolatey for Web apps. It could be curated and provide at least some feedback and reviews that might help with the integrity of applications. Coupled to that could be a native application on each platform that would allow searching and browsing of the registry and then also handle installation in the form of providing the home screen linking, plus maybe an initial security configuration that determines what features are allowed access to for the app. I’m not holding my breath. In order for this sort of thing to take off and gain widespread appeal, a lot of coordination would be required. And in order to get enough traction it would have to come from a well known entity – a mobile Web app store from a no name source is unlikely to gain high enough usage numbers to make a difference. In a way this would eliminate some of the freedom of the Web, but of course this would also be an optional search path in addition to the standard open Web search mechanisms to find and access content today. Security Security is a big deal, and one of the perceived reasons why so many IT professionals appear to be willing to go back to the walled garden of deployed apps is that Apps are perceived as safe due to the official review and curation of the App stores. Curated stores are supposed to protect you from malware, illegal and misleading content. It doesn’t always work out that way and all the major vendors have had issues with security and the review process at some time or another. Security is critical, but I also think that Web applications in general pose less of a security threat than native applications, by nature of the sandboxed browser and JavaScript environments. Web applications run externally completely and in the HTML and JavaScript sandboxes, with only a very few controlled APIs allowing access to device specific features. And as discussed earlier – security for any device interaction can be granted the same for mobile applications through a Web browser, as they can for native applications either via explicit policies loaded from the Web, or via prompting as GeoLocation does today. Security is important, but it’s certainly solvable problem for Web applications even those that need to access device hardware. Security shouldn’t be a reason for Web apps to be an equal player in mobile applications. Apps are winning, but haven’t we been here before? So now we’re finding ourselves back in an era of installed app, rather than Web based and managed apps. Only it’s even worse today than with Desktop applications, in that the apps are going through a gatekeeper that charges a toll and censors what you can and can’t do in your apps. Frankly it’s a mystery to me why anybody would buy into this model and why it’s lasted this long when we’ve already been through this process. It’s crazy… It’s really a shame that this regression is happening. We have the technology to make mobile Web apps much more prominent, but yet we’re basically held back by what seems little more than bureaucracy, partisan bickering and self interest of the major parties involved. Back in the day of the desktop it was Internet Explorer’s 98+%  market shareholding back the Web from improvements for many years – now it’s the combined mobile OS market in control of the mobile browsers. If mobile Web apps were allowed to be treated the same as native apps with simple ways to install and run them consistently and persistently, that would go a long way to making mobile applications much more usable and seriously viable alternatives to native apps. But as it is mobile apps have a severe disadvantage in placement and operation. There are a few bright spots in all of this. Mozilla’s FireFoxOs is embracing the Web for it’s mobile OS by essentially building every app out of HTML and JavaScript based content. It supports both packaged and certified package modes (that can be put into the app store), and Open Web apps that are loaded and run completely off the Web and can also cache locally for offline operation using a manifest. Open Web apps are treated as full class citizens in FireFoxOS and run using the same mechanism as installed apps. Unfortunately FireFoxOs is getting a slow start with minimal device support and specifically targeting the low end market. We can hope that this approach will change and catch on with other vendors, but that’s also an uphill battle given the conflict of interest with platform lock in that it represents. Recent versions of Android also seem to be working reasonably well with mobile application integration onto the desktop and activation out of the box. Although it still uses the Apple meta tags to find icons and behavior settings, everything at least works as you would expect – icons to the desktop on pinning, WebView based full screen activation, and reliable application persistence as the browser/app is treated like a real application. Hopefully iOS will at some point provide this same level of rudimentary Web app support. What’s also interesting to me is that Microsoft hasn’t picked up on the obvious need for a solid Web App platform. Being a distant third in the mobile OS war, Microsoft certainly has nothing to lose and everything to gain by using fresh ideas and expanding into areas that the other major vendors are neglecting. But instead Microsoft is trying to beat the market leaders at their own game, fighting on their adversary’s terms instead of taking a new tack. Providing a kick ass mobile Web platform that takes the lead on some of the proposed mobile APIs would be something positive that Microsoft could do to improve its miserable position in the mobile device market. Where are we at with Mobile Web? It sure sounds like I’m really down on the Mobile Web, right? I’ve built a number of mobile apps in the last year and while overall result and response has been very positive to what we were able to accomplish in terms of UI, getting that final 10% that required device integration dialed was an absolute nightmare on every single one of them. Big compromises had to be made and some features were left out or had to be modified for some devices. In two cases we opted to go the Cordova route in order to get the integration we needed, along with the extra pain involved in that process. Unless you’re not integrating with device features and you don’t care deeply about a smooth integration with the mobile desktop, mobile Web development is fraught with frustration. So, yes I’m frustrated! But it’s not for lack of wanting the mobile Web to succeed. I am still a firm believer that we will eventually arrive a much more functional mobile Web platform that allows access to the most common device features in a sensible way. It wouldn't be difficult for device platform vendors to make Web based applications first class citizens on mobile devices. But unfortunately it looks like it will still be some time before this happens. So, what’s your experience building mobile Web apps? Are you finding similar issues? Just giving up on raw Web applications and building PhoneGap apps instead? Completely skipping the Web and going native? Leave a comment for discussion. Resources Rick Strahl on DotNet Rocks talking about Mobile Web© Rick Strahl, West Wind Technologies, 2005-2014Posted in HTML5  Mobile   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Cisco ASA: How to route PPPoE-assigned subnet?

    - by Martijn Heemels
    We've just received a fiber uplink, and I'm trying to configure our Cisco ASA 5505 to properly use it. The provider requires us to connect via PPPoE, and I managed to configure the ASA as a PPPoE client and establish a connection. The ASA is assigned an IP address by PPPoE, and I can ping out from the ASA to the internet, but I should have access to an entire /28 subnet. I can't figure out how to get that subnet configured on the ASA, so that I can route or NAT the available public addresses to various internal hosts. My assigned range is: 188.xx.xx.176/28 The address I get via PPPoE is 188.xx.xx.177/32, which according to our provider is our Default Gateway address. They claim the subnet is correctly routed to us on their side. How does the ASA know which range it is responsible for on the Fiber interface? How do I use the addresses from my range? To clarify my config; The ASA is currently configured to default-route to our ADSL uplink on port Ethernet0/0 (interface vlan2, nicknamed Outside). The fiber is connected to port Ethernet0/2 (interface vlan50, nicknamed Fiber) so I can configure and test it before making it the default route. Once I'm clear on how to set it all up, I'll fully replace the Outside interface with Fiber. My config (rather long): : Saved : ASA Version 8.3(2)4 ! hostname gw domain-name example.com enable password ****** encrypted passwd ****** encrypted names name 10.10.1.0 Inside-dhcp-network description Desktops and clients that receive their IP via DHCP name 10.10.0.208 svn.example.com description Subversion server name 10.10.0.205 marvin.example.com description LAMP development server name 10.10.0.206 dns.example.com description DNS, DHCP, NTP ! interface Vlan2 description Old ADSL WAN connection nameif outside security-level 0 ip address 192.168.1.2 255.255.255.252 ! interface Vlan10 description LAN vlan 10 Regular LAN traffic nameif inside security-level 100 ip address 10.10.0.254 255.255.0.0 ! interface Vlan11 description LAN vlan 11 Lab/test traffic nameif lab security-level 90 ip address 10.11.0.254 255.255.0.0 ! interface Vlan20 description LAN vlan 20 ISCSI traffic nameif iscsi security-level 100 ip address 10.20.0.254 255.255.0.0 ! interface Vlan30 description LAN vlan 30 DMZ traffic nameif dmz security-level 50 ip address 10.30.0.254 255.255.0.0 ! interface Vlan40 description LAN vlan 40 Guests access to the internet nameif guests security-level 50 ip address 10.40.0.254 255.255.0.0 ! interface Vlan50 description New WAN Corporate Internet over fiber nameif fiber security-level 0 pppoe client vpdn group KPN ip address pppoe ! interface Ethernet0/0 switchport access vlan 2 speed 100 duplex full ! interface Ethernet0/1 switchport trunk allowed vlan 10,11,30,40 switchport trunk native vlan 10 switchport mode trunk ! interface Ethernet0/2 switchport access vlan 50 speed 100 duplex full ! interface Ethernet0/3 shutdown ! interface Ethernet0/4 shutdown ! interface Ethernet0/5 switchport access vlan 20 ! interface Ethernet0/6 shutdown ! interface Ethernet0/7 shutdown ! boot system disk0:/asa832-4-k8.bin ftp mode passive clock timezone CEST 1 clock summer-time CEDT recurring last Sun Mar 2:00 last Sun Oct 3:00 dns domain-lookup inside dns server-group DefaultDNS name-server dns.example.com domain-name example.com same-security-traffic permit inter-interface same-security-traffic permit intra-interface object network inside-net subnet 10.10.0.0 255.255.0.0 object network svn.example.com host 10.10.0.208 object network marvin.example.com host 10.10.0.205 object network lab-net subnet 10.11.0.0 255.255.0.0 object network dmz-net subnet 10.30.0.0 255.255.0.0 object network guests-net subnet 10.40.0.0 255.255.0.0 object network dhcp-subnet subnet 10.10.1.0 255.255.255.0 description DHCP assigned addresses on Vlan 10 object network Inside-vpnpool description Pool of assignable addresses for VPN clients object network vpn-subnet subnet 10.10.3.0 255.255.255.0 description Address pool assignable to VPN clients object network dns.example.com host 10.10.0.206 description DNS, DHCP, NTP object-group service iscsi tcp description iscsi storage traffic port-object eq 3260 access-list outside_access_in remark Allow access from outside to HTTP on svn. access-list outside_access_in extended permit tcp any object svn.example.com eq www access-list Insiders!_splitTunnelAcl standard permit 10.10.0.0 255.255.0.0 access-list iscsi_access_in remark Prevent disruption of iscsi traffic from outside the iscsi vlan. access-list iscsi_access_in extended deny tcp any interface iscsi object-group iscsi log warnings ! snmp-map DenyV1 deny version 1 ! pager lines 24 logging enable logging timestamp logging asdm-buffer-size 512 logging monitor warnings logging buffered warnings logging history critical logging asdm errors logging flash-bufferwrap logging flash-minimum-free 4000 logging flash-maximum-allocation 2000 mtu outside 1500 mtu inside 1500 mtu lab 1500 mtu iscsi 9000 mtu dmz 1500 mtu guests 1500 mtu fiber 1492 ip local pool DHCP_VPN 10.10.3.1-10.10.3.20 mask 255.255.0.0 ip verify reverse-path interface outside no failover icmp unreachable rate-limit 10 burst-size 5 asdm image disk0:/asdm-635.bin asdm history enable arp timeout 14400 nat (inside,outside) source static any any destination static vpn-subnet vpn-subnet ! object network inside-net nat (inside,outside) dynamic interface object network svn.example.com nat (inside,outside) static interface service tcp www www object network lab-net nat (lab,outside) dynamic interface object network dmz-net nat (dmz,outside) dynamic interface object network guests-net nat (guests,outside) dynamic interface access-group outside_access_in in interface outside access-group iscsi_access_in in interface iscsi route outside 0.0.0.0 0.0.0.0 192.168.1.1 1 timeout xlate 3:00:00 timeout conn 1:00:00 half-closed 0:10:00 udp 0:02:00 icmp 0:00:02 timeout sunrpc 0:10:00 h323 0:05:00 h225 1:00:00 mgcp 0:05:00 mgcp-pat 0:05:00 timeout sip 0:30:00 sip_media 0:02:00 sip-invite 0:03:00 sip-disconnect 0:02:00 timeout sip-provisional-media 0:02:00 uauth 0:05:00 absolute timeout tcp-proxy-reassembly 0:01:00 dynamic-access-policy-record DfltAccessPolicy aaa-server SBS2003 protocol radius aaa-server SBS2003 (inside) host 10.10.0.204 timeout 5 key ***** aaa authentication enable console SBS2003 LOCAL aaa authentication ssh console SBS2003 LOCAL aaa authentication telnet console SBS2003 LOCAL http server enable http 10.10.0.0 255.255.0.0 inside snmp-server host inside 10.10.0.207 community ***** version 2c snmp-server location Server room snmp-server contact [email protected] snmp-server community ***** snmp-server enable traps snmp authentication linkup linkdown coldstart snmp-server enable traps syslog crypto ipsec transform-set TRANS_ESP_AES-256_SHA esp-aes-256 esp-sha-hmac crypto ipsec transform-set TRANS_ESP_AES-256_SHA mode transport crypto ipsec transform-set ESP-AES-256-MD5 esp-aes-256 esp-md5-hmac crypto ipsec transform-set ESP-DES-SHA esp-des esp-sha-hmac crypto ipsec transform-set ESP-DES-MD5 esp-des esp-md5-hmac crypto ipsec transform-set ESP-AES-192-MD5 esp-aes-192 esp-md5-hmac crypto ipsec transform-set ESP-3DES-MD5 esp-3des esp-md5-hmac crypto ipsec transform-set ESP-AES-256-SHA esp-aes-256 esp-sha-hmac crypto ipsec transform-set ESP-AES-128-SHA esp-aes esp-sha-hmac crypto ipsec transform-set ESP-AES-192-SHA esp-aes-192 esp-sha-hmac crypto ipsec transform-set ESP-AES-128-MD5 esp-aes esp-md5-hmac crypto ipsec transform-set ESP-3DES-SHA esp-3des esp-sha-hmac crypto ipsec security-association lifetime seconds 28800 crypto ipsec security-association lifetime kilobytes 4608000 crypto dynamic-map outside_dyn_map 20 set pfs group5 crypto dynamic-map outside_dyn_map 20 set transform-set TRANS_ESP_AES-256_SHA crypto dynamic-map SYSTEM_DEFAULT_CRYPTO_MAP 65535 set transform-set ESP-AES-128-SHA ESP-AES-128-MD5 ESP-AES-192-SHA ESP-AES-192-MD5 ESP-AES-256-SHA ESP-AES-256-MD5 ESP-3DES-SHA ESP-3DES-MD5 ESP-DES-SHA ESP-DES-MD5 crypto map outside_map 65535 ipsec-isakmp dynamic SYSTEM_DEFAULT_CRYPTO_MAP crypto map outside_map interface outside crypto isakmp enable outside crypto isakmp policy 1 authentication pre-share encryption 3des hash sha group 2 lifetime 86400 telnet 10.10.0.0 255.255.0.0 inside telnet timeout 5 ssh scopy enable ssh 10.10.0.0 255.255.0.0 inside ssh timeout 5 ssh version 2 console timeout 30 management-access inside vpdn group KPN request dialout pppoe vpdn group KPN localname INSIDERS vpdn group KPN ppp authentication pap vpdn username INSIDERS password ***** store-local dhcpd address 10.40.1.0-10.40.1.100 guests dhcpd dns 8.8.8.8 8.8.4.4 interface guests dhcpd update dns interface guests dhcpd enable guests ! threat-detection basic-threat threat-detection scanning-threat threat-detection statistics host number-of-rate 2 threat-detection statistics port number-of-rate 3 threat-detection statistics protocol number-of-rate 3 threat-detection statistics access-list threat-detection statistics tcp-intercept rate-interval 30 burst-rate 400 average-rate 200 ntp server dns.example.com source inside prefer webvpn group-policy DfltGrpPolicy attributes vpn-tunnel-protocol IPSec l2tp-ipsec group-policy Insiders! internal group-policy Insiders! attributes wins-server value 10.10.0.205 dns-server value 10.10.0.206 vpn-tunnel-protocol IPSec l2tp-ipsec split-tunnel-policy tunnelspecified split-tunnel-network-list value Insiders!_splitTunnelAcl default-domain value example.com username martijn password ****** encrypted privilege 15 username marcel password ****** encrypted privilege 15 tunnel-group DefaultRAGroup ipsec-attributes pre-shared-key ***** tunnel-group Insiders! type remote-access tunnel-group Insiders! general-attributes address-pool DHCP_VPN authentication-server-group SBS2003 LOCAL default-group-policy Insiders! tunnel-group Insiders! ipsec-attributes pre-shared-key ***** ! class-map global-class match default-inspection-traffic class-map type inspect http match-all asdm_medium_security_methods match not request method head match not request method post match not request method get ! ! policy-map type inspect dns preset_dns_map parameters message-length maximum 512 policy-map type inspect http http_inspection_policy parameters protocol-violation action drop-connection policy-map global-policy class global-class inspect dns inspect esmtp inspect ftp inspect h323 h225 inspect h323 ras inspect http inspect icmp inspect icmp error inspect mgcp inspect netbios inspect pptp inspect rtsp inspect snmp DenyV1 ! service-policy global-policy global smtp-server 123.123.123.123 prompt hostname context call-home profile CiscoTAC-1 no active destination address http https://tools.cisco.com/its/service/oddce/services/DDCEService destination address email [email protected] destination transport-method http subscribe-to-alert-group diagnostic subscribe-to-alert-group environment subscribe-to-alert-group inventory periodic monthly subscribe-to-alert-group configuration periodic monthly subscribe-to-alert-group telemetry periodic daily hpm topN enable Cryptochecksum:a76bbcf8b19019771c6d3eeecb95c1ca : end asdm image disk0:/asdm-635.bin asdm location svn.example.com 255.255.255.255 inside asdm location marvin.example.com 255.255.255.255 inside asdm location dns.example.com 255.255.255.255 inside asdm history enable

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