stream(tags).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));
        buf.append(CACHE_KEY_SPLITTER);
        stream(roles).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));
        buf.append(CACHE_KEY_SPLITTER);
        stream(fields).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));

* adjusted http/2 buffer sizes for apiservers to prevent starvation issues between concurrent streams ([#67902](https://github.com/kubernetes/kubernetes/pull/67902), [@liggitt](https://github.com/liggitt))
* Reduced excessive logging from fluentd-gcp-scaler. ([#68837](https://github.com/kubernetes/kubernetes/pull/68837), [@x13n](https://github.com/x13n))

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,

- Reduced CPU and memory consumption of `kube-apiserver` if OpenAPI V2 will not be accessed by any client. Also improved performance of the apiserver on installation of many CRDs. ([#118212](https://github.com/kubernetes/kubernetes/pull/118212), [@Jefftree...

ilter"===i.willChange||t&&i.filter&&"none"!==i.filter)return n;n=n.parentNode}return null}(e)||n}var D="top",A="bottom",L="right",P="left",M="auto",k=[D,A,L,P],W="start",B="end",H="viewport",T="popper",R=k.reduce((function(e,t){return e.concat([t+"-"+W,t+"-"+B])}),[]),S=[].concat(k,[M]).reduce((function(e,t){return e.concat([t,t+"-"+W,t+"-"+B])}),[]),V=["beforeRead","read","afterRead","beforeMain","main","afterMain","beforeWrite","write","afterWrite"];function q(e){var t=new Map,n=new Set,r=[];function...

			"?Action=AssumeRoleWithLDAPIdentity&LDAPUsername=myusername&LDAPPassword=*REDACTED*&Version=2011-06-15",
		},
		{
			"LDAPPassword=can+youreadthis%3F&Version=2011-06-15&?Action=AssumeRoleWithLDAPIdentity&LDAPUsername=myusername",
			"LDAPPassword=*REDACTED*&Version=2011-06-15&?Action=AssumeRoleWithLDAPIdentity&LDAPUsername=myusername",
		},
		{

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

        case LOCK:
            return "Release existing locks or wait for them to be released";
        case BUFFER:
            return "Reduce buffer usage or increase buffer pool size";
        case THREAD_POOL:
            return "Reduce concurrent operations or increase thread pool size";
        default:
            return "Check resource availability and retry";
        }
    }

    /**

    public static final int IMP_ERR = 0x4;
    /** Refused error in the name service */
    public static final int RFS_ERR = 0x5;
    /** Active error in the name service */
    public static final int ACT_ERR = 0x6;
    /** Name in conflict error */
    public static final int CFT_ERR = 0x7;

    // session service error codes
    /** Connection refused by the remote host */
    public static final int CONNECTION_REFUSED = -1;

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk