vania-janet/multiturn-rag-retrieval-data huggingface dataset:
EU Central (ams03, mil01, par01, fra02, fra04, fra05) 166.9.28.107, 166.9.28.17, 166.9.28.19, 166.9.28.20, 166.9.28.203, 166.9.28.22, 166.9.28.23, 166.9.28.235, 166.9.28.24, 166.9.28.240, 166.9.28.43, 166.9.28.64, 166.9.28.84, 166.9.28.87, 166.9.28.91, 166.9.28.94, 166.9.28.95, 166.9.30.100, 166.9.30.11, 166.9.30.116, 166.9.30.12, 166.9.30.13, 166.9.30.22, 166.9.30.41, 166.9.30.54, 166.9.30.56, 166.9.30.9, 166.9.30.92, 166.9.32.101, 166.9.32.185, 166.9.32.20, 166.9.32.26, 166.9.32.27, 166.9.32.44, 166.9.32.54, 166.9.32.56, 166.9.32.84, 166.9.32.88, 166.9.32.9, 166.9.248.77, 166.9.248.106, 166.9.248.137 
 Osaka (osa21, osa22, osa23) 166.9.70.6, 166.9.70.8, 166.9.71.8, 166.9.71.10, 166.9.72.9, 166.9.72.10, 166.9.247.41, 166.9.247.75, 166.9.247.107
<saml:Assertion xmlns:saml="urn:oasis:names:tc:SAML:2.0:assertion" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" Version="2.0" ID="pfx539c9774-de5c-5f52-0c3f-b1c2e2697a89" IssueInstant="2018-01-29T13:02:58Z" xmlns:samlp="urn:oasis:names:tc:SAML:2.0:protocol">
<saml:Issuer>idp_entityId</saml:Issuer>
<ds:Signature xmlns:ds="http://www.w3.org/2000/09/xmldsig">
<ds:SignedInfo>
<ds:CanonicalizationMethod Algorithm="one_of_supported_algo"/>
<ds:SignatureMethod Algorithm="one_of_supported_algo"/>
<ds:Reference URI="pfx539c9774-de5c-5f52-0c3f-b1c2e2697a89">
<ds:Transforms>
<ds:Transform Algorithm="one_of_supported_algo"/>
<ds:Transform Algorithm="one_of_supported_algo"/>
</ds:Transforms>
<ds:DigestMethod Algorithm="one_of_supported_algo"/>
<ds:DigestValue>huywDPPfOEGyyzE7d5hjOG97p7FDdGrjoSfes6RB19g=</ds:DigestValue>
</ds:Reference>
</ds:SignedInfo>
"toolchain_crn": "crn:v1:bluemix:public:toolchain:us-south:a/190e0b4ce4cd013159917665213ddc51:7467a214-f404-4e23-acab-2e57e935d138::" // CRN of the toolchain
"pipeline_id": "b1cfb6a4-813d-4fec-bbf7-ce662e8ce4e0", // the id of the pipeline that generated this piece of evidence
"pipeline_run_id": 12345, // this is the id of the pipeline run that generated this piece of evidence
"artifact_name": "mydependentpackage", //name of the dependent artifact
"checksum": "324334", // checksum or digital isgnature
"verified": "yes"|"no"], // checksum verified or not
"scan_status": "passed"|"failed"], // the scan of the status
"log": {
"url": "https://acme.org/log.txt", // the link to where the actual log of the test is
"hash": "abc123", // the hash of the log file
},
"checksum_issues": 134, 132, 342], // link(s) to the issues representing missing or not matching checksums
"scan_issues": 123, 342, 453], // link(s) to the issues found by the scan (X-Ray)
"expired_issues": 134, 132, 342], // link(s) to the issues for which the grace period expired
"required": "yes"|"no"]// yes: this is an auditable step; no: this is a non auditable step
// there could be other artifacts that serve as build input
}, {
// build output: images or config files, calico policies...
String matching 

You can match the values of a string time series against a string sequence (sequence matching) or against a set of strings (set matching).

Because time series observations are necessarily chronological, the values of a string time series form a sequence, that is, a series of values that occur one after the other in time. Each sequence contains sublists(values are contiguous) and subsequences (values are in order but are not necessarily contiguous). For example:

The sublists of the sequence [a,b,c,d] are the following:

[a,b,c,d] [a,b,c] [a,b] [a] [b,c,d] [b,c] [b] [c,d] [c] [d]

The subsequences of the sequence [a,b,c,d] are the sublists that are described previously, plus the following noncontiguous sequences:

[a,b,d] [a,c,d] [a,c] [b,d] [a,d]

Each subsequence has a corresponding match sublist, which is the sublist that corresponds to the subsequence but that includes any missing contiguous values. For example, for the sequence [a,b,c,d,e], the subsequences [a,b,d], [a,c,d], and [a,d] all have the same match sublist, which is [a,b,c,d].



 Sequence matching 

For sequence matching, all the members of the specified array of string values (the pattern) must match, in the order specified, values in the input string time series. If any of the pattern values do not occur in the time series, or if they occur but are out of order, the matching function returns null. Each matching function specifies a matcher artifact, which determines the behavior of the matching function:



* Whether the matching function is to match the pattern with the entire sequence of time series values, with a sublist of the sequence, or with a subsequence.
* The threshold that must be met for a match to be considered successful.
18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":18],"sequence":{"ch":"m18","label":"M_m18","name":"parametric_pulse","parameters":{"amp":0.04695872889448773,-0.35692419052316177],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m18","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":19],"sequence":{"ch":"m19","label":"M_m19","name":"parametric_pulse","parameters":{"amp":-0.33603973513852214,-0.09786366234747149],"duration":2240
If any of the pattern values do not occur in the time series, or if they occur but are out of order, the matching function returns null. Each matching function specifies a matcher artifact, which determines the behavior of the matching function:



* Whether the matching function is to match the pattern with the entire sequence of time series values, with a sublist of the sequence, or with a subsequence.
* The threshold that must be met for a match to be considered successful. Depending on the matcher that is used, it is based on one of the following ratios:



* The length of the pattern (P) divided by the length of the entire sequence (S).
* The length of the pattern (P) divided by the length of the match sublist (M).
* The length of the match sublist (M) divided by the length of the entire sequence (S).



* Whether the matching function returns the observations that correspond to the pattern or to the match string.



For example, consider the following string time series that contains five observations that is stored in a table column with the name ts4:

[(0,'a'), (1,'b'), (3,'c'), (7,'d'), (8,'e')]



* The following SELECT statement returns the matching observations because the specified array matches ts4 exactly.

SELECT TS_MATCH(ts4, TS_MATCHER_SEQ(), ARRAY('a','b','c','d','e'))

Result:

[(0,'a'), (1,'b'), (3,'c'), (7,'d'), (8,'e')]
* The following SELECT statement returns null because although all members of the specified array occur in ts4 in the correct order, the array does not match ts4 exactly.

SELECT TS_MATCH(ts4, TS_MATCHER_SEQ(), ARRAY('a','b','c'))

Result:

NaN
* The following SELECT statement returns the matching observations. The array [b,c,d] is a match because the following conditions are satisfied:
,0.00880710780620575,0.000028597418349818327],0.00856779608875513,0.00002782035335258115],0.008330208249390125,0.000027048885385738686],0.0080944262444973,0.00002628328002174385],0.007860527373850346,0.000025523790100123733],0.007628586608916521,0.000024770659365458414],0.007398677058517933,0.000024024124286370352],0.007170867640525103,0.000023284408598556183],0.0069452254101634026,0.00002255172876175493],0.006721813231706619,0.000021826292140758596],0.006500691641122103,0.000021108291548443958],0.0062819188460707664,0.000020397916159708984],0.006065548397600651,0.000019695344235515222],0.0058516329154372215,0.000019000743122887798],0.005640220828354359,0.00001831427107390482
":"GHz","value":5.117666721552654},{"date":"2022-03-29T16:20:11Z","name":"anharmonicity","unit":"GHz","value":-0.34132883465309716},{"date":"2022-03-29T04:38:44Z","name":"readout_error","unit":"","value":0.016000000000000014},{"date":"2022-03-29T04:38:44Z","name":"prob_meas0_prep1","unit":"","value":0.0226},{"date":"2022-03-29T04:38:44Z","name":"prob_meas1_prep0","unit":"","value":0.009399999999999964},{"date":"2022-03-29T04:38:44Z","name":"readout_length","unit":"ns","value":856.8888888888888}],{"date":"2022-03-29T16:01:26Z","name":"T1","unit"
* [VPC reference architecture](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-shared-monitoring-compliance)
* [Satellite reference architecture](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-shared-monitoring-compliance)



Most Relevant Controls:



Table 18. Related controls for monitoring security and compliance

 Family Control 

 Change Management (CM) [CM-2 (2) Baseline Configuration | Automation Support for Accuracy and Currency](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cm-2.2) <br>[CM-6 Configuration Settings](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cm-6) <br>[CM-6 (1) Configuration Settings | Automated Management, Application, and Verification](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cm-6.1) 







 Next steps 

Learn about the three reference architectures for the IBM Cloud for Financial Services:



* [VPC reference architecture](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-vpc-architecture-about)
* [Satellite reference architecture](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-satellite-architecture-about)
* [VMware Regulated Workloads reference architecture](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-vmware-overview)



Or, if you're intending to be a software provider, see also:



* [Best practices for software](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-best-practices-software)
[Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) 
 Redundant anchors peers on a channel ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) 
 Anti-affinity (peers) !
_est":[7.182176913,7.285029648,7.15665742,7.282378059,7.230041176,7.235190703,7.354339834,7.0996369,7.096402354,7.319290223,7.168399425,7.224047042,7.329061511,7.21721936,7.275359312,7.277010405,7.153391272,7.345788554,7.103128053,7.086879072,7.325129903,7.225602481,7.214742524,7.281663739,7.140193727,7.263512387,7.133120588],"meas_kernel":{"name":"hw_qmfk","params":{}},"pulse_library":[{"name":"CX_d10_u24","samples":-0.00003798729449044913,-0.000007468183412129292],-0.0000768694662838243,-0.000015112296750885434],-0.00011665541387628764,-0.000022934089429327287],-0.00015735365741420537,-0.000030935236281948164],-0.00019897226593457162,-0.00003911732346750796],-0.00024151890829671174,-0.000047481862566201016],-0.00028500077314674854,-0.00005603026147582568],-
:"control"},"u54":{"operates":{"qubits":[25,26]},"purpose":"cross-resonance","type":"control"},"u55":{"operates":{"qubits":[26,25]},"purpose":"cross-resonance","type":"control"},"u6":{"operates":{"qubits":[3,2]},"purpose":"cross-resonance","type":"control"},"u7":{"operates":{"qubits":[3,5]},"purpose":"cross-resonance","type":"control"},"u8":{"operates":{"qubits":[4,1]},"purpose":"cross-resonance","type":"control"},"u9":{"operates":{"qubits":[4,7]},"purpose":"cross-resonance","type":"control"}},"clops":2207,"conditional":false,"conditional_latency":[],"coupling_map":[0,1],1,0],1,2],1,4],2,1],2,3],3,2],3,5],4,1],4,7],5,3],5,8],6,7],7,4],7,6],7,10],8,5],8,9],8,11],9,8],10,7],10,12],11,8],11,14],12,10],12,13],12,15],13,12],13,14],14,11],14,13],14,16],15,12],
compliance.posture-management-profiles-attachments.delete Delete an attachment. 
 compliance.posture-management-profiles-attachments-parameters.list View the parameters that are associated with a profile attachement. 
 compliance.posture-management-profiles-attachments-parameters.create Set a parameter variable for an attachment. 
 compliance.posture-management-profiles-attachments-parameters.read View the parameter details. 
 compliance.posture-management-profiles-attachments-parameters.update Update a paramater value for an attachment. 
 compliance.posture-management-control-libraries.list View the available control libraries in your account. 
 compliance.posture-management-control-libraries.create Create a new control library. 
 compliance.posture-management-control-libraries.read View the details of a control library. 
 compliance.posture-management-control-libraries.update Update a control library. 
 compliance.posture-management-control-libraries.delete Delete a control library. 
 compliance.posture-management-scans.create Initiate a scan. 
 compliance.posture-management-reports.list View all available results. 
 compliance.posture-management-reports.create Generate a new report. 
 compliance.posture-management-reports.read View detailed results. 
 compliance.posture-management-reports.delete Delete results. 
 compliance.configuration-governance-rules.list View the available rules in Security and Compliance Center. 
 compliance.configuration-governance-rules.create Create a new rule. 
 compliance.configuration-governance-rules.read View the details of a rule. 
 compliance.configuration-governance-rules.update Update a rule. 
 compliance.configuration-governance-rules.delete Delete a rule. 
 compliance.admin-settings.list View Security and Compliance Center settings for your account. 
 compliance.admin-settings.update Update Security and Compliance Center settings for your account. 
 compliance.admin-test-event.send Send a test event to a connected Event Notifications service instance.
,-73.93 40.881,-73.924 40.882,-73.919 40.879,-73.909 40.877,-73.906 40.873,-73.907 40.867,-73.912 40.86,-73.918 40.855,-73.931 40.835,-73.93 40.81,-73.925 40.803,-73.925 40.795,-73.939 40.781,-73.938 40.774,-73.968 40.741,-73.969 40.733,-73.967 40.73,-73.967 40.726,-73.969 40.717,-73.973 40.709,-73.977 40.706,-73.996 40.705,-73.999 40.701,-74.008 40.696,-74.016 40.696,-74.017 40.698,-74.019 40.698,-74.023 40.703,-74.023 40.709))'
wkt_reader = stc.wkt_reader()
westchester = wkt_reader.read(westchester_WKT)
white_plains = wkt_reader.read(white_plains_WKT)
manhattan = wkt_reader.read(manhattan_WKT)
Show more



 Topological relations 

You can use the following topological relation functions:



* contains: Returns true if the given geometry is completely contained by this geometry. contains returns the exact opposite of within.

>>> westchester.contains(white_plains)
True
* within: Returns true if the given geometry is completely within this geometry. within returns the exact opposite of contains.

>>> white_plains.within(westchester)
True
* intersects: Returns true if the intersection does not result in an empty set. intersects is the opposite of disjoint.

>>> westchester.intersects(manhattan)
False
csi-rbdplugin-nkzcd 3/3 Running 0 8m12s
csi-rbdplugin-provisioner-75596f49bd-7mk5g 5/5 Running 0 8m12s
csi-rbdplugin-provisioner-75596f49bd-r2p6g 5/5 Running 0 8m12s
noobaa-core-0 1/1 Running 0 4m37s
noobaa-db-0 1/1 Running 0 4m37s
noobaa-endpoint-7d959fd6fb-dr5x4 1/1 Running 0 2m27s
noobaa-operator-6cbf8c484c-fpwtt 1/1 Running 0 9m41s
ocs-operator-9d6457dff-c4xhh 1/1 Running 0 9m42s
rook-ceph-crashcollector-169.48.170.83-89f6d7dfb-gsglz 1/1 Running 0 5m38s
rook-ceph-crashcollector-169.48.170.88-6f58d6489-b9j49 1/1 Running 0 5m29s
rook-ceph-crashcollector-169.48.170.90-866b9d444d-zk6ft 1/1 Running 0 5m15s
rook-ceph-drain-canary-169.48.170.83-6b885b94db-wvptz 1/1 Running 0 4m41s
rook-ceph-drain-canary-169.48.170.88-769f8b6b7-mtm47 1/1 Running 0 4m39s
rook-ceph-drain-canary-169.48.170.90-84845c98d4-pxpqs 1/1 Running 0 4m40s
rook-ceph-mds-ocs-storagecluster-cephfilesystem-a-6dfbb4fcnqv9g 1/1 Running 0 4m16s
2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m1","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":2],"sequence":{"ch":"m2","label":"M_m2","name":"parametric_pulse","parameters":{"amp":-0.18486217867866572,0.19678916355881254],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m2","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,
20:11Z","name":"frequency","unit":"GHz","value":4.88820378032764},{"date":"2022-03-29T16:20:11Z","name":"anharmonicity","unit":"GHz","value":-0.344373616967254},{"date":"2022-03-29T04:38:44Z","name":"readout_error","unit":"","value":0.008099999999999996},{"date":"2022-03-29T04:38:44Z","name":"prob_meas0_prep1","unit":"","value":0.011600000000000055},{"date":"2022-03-29T04:38:44Z","name":"prob_meas1_prep0","unit":"","value":0.0046},{"date":"2022-03-29T04:38:44Z","name":"readout_length","unit":"ns","value":856.8888888888888}],{"date":"2022-03-29T16:
parameters":{"amp":0.10515991526809636,0.002363533204347135],"beta":-2.1494178582769803,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"sx","qubits":19],"sequence":{"ch":"d19","label":"X90p_d19","name":"parametric_pulse","parameters":{"amp":0.10435209762961972,0.0004178817129556554],"beta":-1.0957411302075677,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"sx","qubits":20],"sequence":{"ch":"d20","label":"X90p_d20","name":"parametric_pulse","parameters":{"amp":0.08906994860879265,0.0012405319959103217],"beta":-0.5292445408258966,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"sx","qubits":21],"sequence":{"ch":"d21","label":"X90p_d21","name":"parametric_pulse","parameters":{"amp":0.10054543378303288,
","unit":"GHz","value":5.018601692764167},{"date":"2022-03-29T16:20:11Z","name":"anharmonicity","unit":"GHz","value":-0.3422121157217532},{"date":"2022-03-29T04:38:44Z","name":"readout_error","unit":"","value":0.007600000000000051},{"date":"2022-03-29T04:38:44Z","name":"prob_meas0_prep1","unit":"","value":0.0088},{"date":"2022-03-29T04:38:44Z","name":"prob_meas1_prep0","unit":"","value":0.006399999999999961},{"date":"2022-03-29T04:38:44Z","name":"readout_length","unit":"ns","value":856.8888888888888}],{"date":"2022-03-29T16:01:26Z","name":"T1"
,"beta":-2.238660027843719,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"x","qubits":4],"sequence":{"ch":"d4","label":"Xp_d4","name":"parametric_pulse","parameters":{"amp":0.18851238067625303,0],"beta":-0.70563846860603,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"x","qubits":5],"sequence":{"ch":"d5","label":"Xp_d5","name":"parametric_pulse","parameters":{"amp":0.18780121182564427,0],"beta":-0.8715805033660862,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"x","qubits":6],"sequence":{"ch":"d6","label":"Xp_d6","name":"parametric_pulse","parameters":{"amp":0.19660906820349777,0],"beta":-1.0090323722529313,"duration":160
,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":14],"sequence":{"ch":"m14","label":"M_m14","name":"parametric_pulse","parameters":{"amp":0.24405471924768304,0.304199431315929],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m14","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":15],"sequence":{"ch":"m15","label":
,0.0001133451223722659],-0.005335554946213961,0.00011701942275976762],-0.005258250515908003,0.00012061246525263414],-0.00518267834559083,0.00012412497017066926],-0.0051088230684399605,0.00012755767966154963],-0.0049645123071968555,0.000134265108499676],-0.004890657030045986,0.00013769781799055636],-0.004815084859728813,0.00014121031563263386],-0.004737779963761568,0.0001448033726774156],-0.00465872697532177,0.00014847767306491733],-0.0045779114589095116,0.00015223390073515475],-0.004495318979024887,0.00015607269597239792],-0.004410937428474426,0.00015999465540517122
:"parametric_pulse","parameters":{"amp":-0.24827290464195859,0.07720469429150624],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m3","duration":1616,"name":"delay","t0":2240},{"ch":"m4","label":"M_m4","name":"parametric_pulse","parameters":{"amp":-0.13039505703052454,-0.25903113539110745],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m4","duration":1616,"name":"delay","t0":2240},{"ch":"m5","label":"M_m5","name":"parametric_pulse","parameters":{"amp":-0.2602175328900784,-0.24877064854319902],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m5","duration":1616,"name":"delay
AP South (syd01, syd04, syd05) 130.198.64.19, 130.198.66.26, 130.198.79.170, 130.198.83.34, 130.198.102.82, 135.90.66.2, 135.90.68.114, 135.90.69.66, 135.90.69.82, 135.90.89.234, 168.1.6.106, 168.1.8.195, 168.1.12.98, 168.1.39.34, 168.1.58.66 
 EU Central (ams03, mil01, par01, fra02, fra04, fra05) 149.81.103.98, 149.81.104.122, 149.81.113.154, 149.81.123.18, 149.81.142.90, 149.81.180.114, 149.81.180.122, 149.81.68.2, 149.81.78.114, 158.177.102.162, 158.177.107.50, 158.177.112.146, 158.177.138.138, 158.177.151.2, 158.177.156.178, 158.177.198.138, 158.177.79.34, 159.122.141.69, 159.122.150.2, 159.8.79.250, 159.8.86.149, 159.8.95.34, 161.156.115.138, 161.156.120.74, 161.156.12.82, 161.156.183.218, 161.156.187.226, 161.156.65.42, 161.156.65.82, 161.156.74.10, 161.156.79.26, 169.50.146.82, 169.50.169.110, 169.50.184.18, 169.50.56.174, 169.51.197.18
) AS rank
FROM VALUES
(1, 1 ,100),
(2, 1 ,50),
(8, 1 ,250),
(3, 2 ,200),
(4, 2 ,300),
(9, 2 ,1000),
(5, 3 ,300),
(6, 3 ,100),
(7, 3 ,400) AS posts
) WHERE rank <= 2
Show more

The result of the example query is shown in the following table.



Table 27. Query result for example: derive posts ranking by using an inline window specification.

 EMP_ID DEPT_ID POSTS RANK 

 8 1 250 1 
 1 1 100 2 
 7 3 400 1 
 5 3 300 2 
 9 2 1000 1 
 4 2 300 2 







 Analytic function example 

This example uses a table that contains transaction information. The layout is as follows:



* Column 1: transaction ID
* Column 2: account number
* Column 3: transaction amount



The example shows how to create a cumulative distribution of transaction amounts by using the analytic function CUME_DIST(). The CUME_DIST() function returns the percentage of rows that have a value less than or equal to the current row’s value.

-- cumulative distribution of transaction amounts
SELECT
txn_amount,
MAX(balance_dist)
FROM (
SELECT
txn.col2 AS account,
txn.col3 AS txn_amount,
CUME_DIST() OVER current_balance AS balance_dist
FROM VALUES
(1, 42, 1000),
(2, 4711, 2000),
(3, 42, -200),
(4, 42, -200),
(5, 4711, 1000),
(6, 4711, -300),
(7, 4711, -300),
(8, 42, 1000),
(9, 4711, -400) AS txn
WINDOW current_balance AS (ORDER BY txn.col3)
ORDER BY txn.col3