[
    {
        "clusterName": "agent",
        "endpoints": [
            {
                "locality": {},
                "lbEndpoints": [
                    {
                        "endpoint": {
                            "address": {
                                "socketAddress": {
                                    "address": "127.0.0.1",
                                    "portValue": 15020
                                }

    loadBalancingWeight: 3
    locality: {}
  policy:
    overprovisioningFactor: 140
- clusterName: sds-grpc
  endpoints:
  - lbEndpoints:
    - endpoint:
        address:
          pipe:
            path: ./var/run/secrets/workload-spiffe-uds/socket
        healthCheckConfig: {}
      healthStatus: HEALTHY
      loadBalancingWeight: 1
    locality: {}
  policy:
    overprovisioningFactor: 140

   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these
   * retries, there is increased contention and reduced locality,
   * which is still better than alternatives.
   *
   * Per-thread hash codes are initialized to random values.
   * Contention and/or table collisions are indicated by failed

   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these
   * retries, there is increased contention and reduced locality,
   * which is still better than alternatives.
   *
   * Per-thread hash codes are initialized to random values.
   * Contention and/or table collisions are indicated by failed

 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Long>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code LongStream} features (like {@link LongStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToLong(v -> v)}.

 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Integer>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code IntStream} features (like {@link IntStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToInt(v -> v)}.
 * </ul>

 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Integer>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code IntStream} features (like {@link IntStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToInt(v -> v)}.
 * </ul>

 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Double>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code DoubleStream} features (like {@link DoubleStream#sum}) using {@code
 *       stream()} instead of the awkward {@code stream().mapToDouble(v -> v)}.

## Version 4.4.1

_2020-03-08_

 *  Fix: Don't reuse a connection on redirect if certs match but DNS does not. For better
    locality and performance OkHttp attempts to use the same pooled connection across redirects and
    follow-ups. It independently shares connections when the IP addresses and certificates match,

    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality:
    // benchmarking shows it can take 7 times longer than the other two in some cases. The other two
    // do n swaps, minus a delta (0 or 2 for Reversal, gcd(d, n) for Successive), so that's about