Karthikey Hegde dracarys18

## keywords.json
[
  {
    "keyword": "Distributed Systems",
    "context_string": "Keyword: Distributed Systems. Context: Architectures, patterns, and principles for coordinating multiple independent computing nodes across a network to operate as a single coherent system. Focuses on data replication, partitioning, and consistency models. CAP theorem, PACELC theorem, eventual consistency, strong consistency, causal consistency, read-after-write consistency, monotonic reads, linearizability, strict serializability. Replication strategies include single-leader, multi-leader, and leaderless replication, strict quorums, sloppy quorums, hinted handoff, read repair, replication lag, active-active deployments. Data partitioning, sharding, consistent hashing, distributed hash tables DHT. Concurrency and time management encompass vector clocks, version vectors, Lamport timestamps, hybrid logical clocks HLC, TrueTime bounded clock uncertainty, clock skew, clock drift, monotonic vs wall clocks, Network Time Protocol NTP, deterministic a

## expression.rs
#[derive(Default)]
struct SelectBuilder {
    table: String,
}

impl SelectBuilder {
    fn new(table: String) -> Self {
        Self { table }
    }

## xlock.java
public class ConcurrencyMgr {

   /**
    * The global lock table. This variable is static because
    * all transactions share the same table.
    */
   private static LockTable locktbl = new LockTable();
   private Map<BlockId,String> locks  = new HashMap<BlockId,String>();

      /**

## xlock.rs

    /// If the transaction already has an exclusive lock or a shared lock then
    /// the lock will not be acquired. Otherwise we acquire the shared lock and
    /// then upgrade the lock to Exclusive
    pub fn xlock(&mut self, block: &Block) -> DbResult<()> {
        if !self.has_x_lock(block) {
            self.slock(block)?;
            LOCK_TABLE.safe_lock().xlock(block)?;
            self.locks.insert(block.clone(), LockTypes::Exclusive);
        }

## myfeed.opml
<?xml version="1.0" encoding="UTF-8"?>
<opml version="1.1" xmlns:feeder="https://nononsenseapps.com/feeder">
  <head>
    <title>
      Feeder
    </title>
  </head>
  <body>
    <outline title="Companies" text="Companies">
      <outline feeder:notify="false" feeder:imageUrl="https://cdn-images-1.medium.com/proxy/1*TGH72Nnw24QL3iV9IOm4VA.png" feeder:fullTextByDefault="false" feeder:openArticlesWith="" feeder:alternateId="false" title="The Netflix Tech Blog" text="The Netflix Tech Blog" type="rss" xmlUrl="http://techblog.netflix.com/feeds/posts/default"/>

## state_population.json
{
    "AP": {
        "codename": "AP",
        "id": 21,
        "name": "Andhra Pradesh",
        "population": "53,903,393"
    },
    "AR": {
        "codename": "AR",
        "id": 32,
	[
	{
	"keyword": "Distributed Systems",
	"context_string": "Keyword: Distributed Systems. Context: Architectures, patterns, and principles for coordinating multiple independent computing nodes across a network to operate as a single coherent system. Focuses on data replication, partitioning, and consistency models. CAP theorem, PACELC theorem, eventual consistency, strong consistency, causal consistency, read-after-write consistency, monotonic reads, linearizability, strict serializability. Replication strategies include single-leader, multi-leader, and leaderless replication, strict quorums, sloppy quorums, hinted handoff, read repair, replication lag, active-active deployments. Data partitioning, sharding, consistent hashing, distributed hash tables DHT. Concurrency and time management encompass vector clocks, version vectors, Lamport timestamps, hybrid logical clocks HLC, TrueTime bounded clock uncertainty, clock skew, clock drift, monotonic vs wall clocks, Network Time Protocol NTP, deterministic a
	#[derive(Default)]
	struct SelectBuilder {
	table: String,
	}

	impl SelectBuilder {
	fn new(table: String) -> Self {
	Self { table }
	}
	public class ConcurrencyMgr {

	/**
	* The global lock table. This variable is static because
	* all transactions share the same table.
	*/
	private static LockTable locktbl = new LockTable();
	private Map<BlockId,String> locks = new HashMap<BlockId,String>();

	/**

	/// If the transaction already has an exclusive lock or a shared lock then
	/// the lock will not be acquired. Otherwise we acquire the shared lock and
	/// then upgrade the lock to Exclusive
	pub fn xlock(&mut self, block: &Block) -> DbResult<()> {
	if !self.has_x_lock(block) {
	self.slock(block)?;
	LOCK_TABLE.safe_lock().xlock(block)?;
	self.locks.insert(block.clone(), LockTypes::Exclusive);
	}
	<?xml version="1.0" encoding="UTF-8"?>
	<opml version="1.1" xmlns:feeder="https://nononsenseapps.com/feeder">
	<head>
	<title>
	Feeder
	</title>
	</head>
	<body>
	<outline title="Companies" text="Companies">
	<outline feeder:notify="false" feeder:imageUrl="https://cdn-images-1.medium.com/proxy/1*TGH72Nnw24QL3iV9IOm4VA.png" feeder:fullTextByDefault="false" feeder:openArticlesWith="" feeder:alternateId="false" title="The Netflix Tech Blog" text="The Netflix Tech Blog" type="rss" xmlUrl="http://techblog.netflix.com/feeds/posts/default"/>
	{
	"AP": {
	"codename": "AP",
	"id": 21,
	"name": "Andhra Pradesh",
	"population": "53,903,393"
	},
	"AR": {
	"codename": "AR",
	"id": 32,